1
The genetic architecture of the human cerebral cortex.
Katrina L. Grasby
1
*, Neda Jahanshad
2
*, Jodie N. Painter
1
, Lucía Colodro-Conde
1
, Janita Bralten
3,4
, Derrek P.
Hibar
2,5
, Penelope A. Lind
1
, Fabrizio Pizzagalli
2
, Christopher R.K. Ching
2,6
, Mary Agnes B. McMahon
2
, Natalia
Shatokhina
2
, Leo C.P. Zsembik
7
, Ingrid Agartz
8,9,10,11
, Saud Alhusaini
12,13
, Marcio A.A. Almeida
14
, Dag
Alnæs
8,9
, Inge K. Amlien
15
, Micael Andersson
16,17
, Tyler Ard
18
, Nicola J. Armstrong
19
, Allison Ashley-Koch
20
,
Manon Bernard
21
, Rachel M. Brouwer
22
, Elizabeth E.L. Buimer
22
, Robin Bülow
23
, Christian Bürger
24
, Dara M.
Cannon
25
, Mallar Chakravarty
26,27
, Qiang Chen
28
, Joshua W. Cheung
2
, Baptiste Couvy-Duchesne
29,30,31
,
Anders M. Dale
32,33
, Shareefa Dalvie
34
, Tânia K. de Araujo
35
, Greig I. de Zubicaray
36
, Sonja M.C. de Zwarte
22
,
Anouk den Braber
37,38
, Nhat Trung Doan
8,9
, Katharina Dohm
24
, Stefan Ehrlich
39
, Hannah-Ruth Engelbrecht
40
,
Susanne Erk
41
, Chun Chieh Fan
42
, Iryna O. Fedko
37
, Sonya F. Foley
43
, Judith M. Ford
44
, Masaki Fukunaga
45
,
Melanie E. Garrett
20
, Tian Ge
46,47
, Sudheer Giddaluru
48
, Aaron L. Goldman
28
, Nynke A. Groenewold
34
, Dominik
Grotegerd
24
, Tiril P. Gurholt
8,9,10
, Boris A. Gutman
2,49
, Narelle K. Hansell
31
, Mathew A. Harris
50,51
, Marc B.
Harrison
2
, Courtney C. Haswell
52,53
, Michael Hauser
20
, Dirk J. Heslenfeld
54
, David Hoehn
55
, Laurena
Holleran
25
, Martine Hoogman
3,4
, Jouke-Jan Hottenga
37
, Masashi Ikeda
56
, Deborah Janowitz
57
, Iris E.
Jansen
58,59
, Tianye Jia
60,61,62
, Christiane Jockwitz
63,64,65
, Ryota Kanai
66,67,68
, Sherif Karama
69,70,26
, Dalia
Kasperaviciute
71
, Tobias Kaufmann
8,9
, Sinead Kelly
72,73
, Masataka Kikuchi
74
, Marieke Klein
3,4,22
, Michael
Knapp
75
, Annchen R. Knodt
76
, Bernd Krämer
77,78
, Thomas M. Lancaster
43,79
, Phil H. Lee
46,80
, Tristram A. Lett
41
,
Lindsay B. Lewis
81,70
, Iscia Lopes-Cendes
35,82
, Michelle Luciano
83,84
, Fabio Macciardi
85
, Andre F. Marquand
86,4
,
Samuel R. Mathias
87,88
, Tracy R. Melzer
89,90,91
, Yuri Milaneschi
92
, Nazanin Mirza-Schreiber
55
, Jose C.V.
Moreira
82,93
, Thomas W. Mühleisen
63,94,95
, Bertram Müller-Myhsok
55,96,97
, Pablo Najt
25
, Soichiro Nakahara
85,98
,
Kwangsik Nho
99
, Loes M. Olde Loohuis
100
, Dimitri Papadopoulos Orfanos
101
, John F. Pearson
102,103
, Toni L.
Pitcher
89,90,91
, Benno Pütz
55
, Anjanibhargavi Ragothaman
2
, Faisal M. Rashid
2
, Ronny Redlich
24
, Céline S.
Reinbold
94,104
, Jonathan Repple
24
, Geneviève Richard
8,9,105,106
, Brandalyn C. Riedel
2,99
, Shannon L. Risacher
99
,
Cristiane S. Rocha
35,82
, Nina Roth Mota
3,107,4
, Lauren Salminen
2
, Arvin Saremi
2
, Andrew J. Saykin
99,108
, Fenja
Schlag
109
, Lianne Schmaal
110,111,112
, Peter R. Schofield
113,114
, Rodrigo Secolin
35,82
, Chin Yang Shapland
109
, Li
Shen
115
, Jean Shin
21,116
, Elena Shumskaya
3,117,4
, Ida E. Sønderby
8,9
, Emma Sprooten
4
, Lachlan T. Strike
31
,
Katherine E. Tansey
79
, Alexander Teumer
118
, Anbupalam Thalamuthu
119
, Sophia I. Thomopoulos
2
, Diana
Tordesillas-Gutiérrez
120,121
, Jessica A. Turner
122,123
, Anne Uhlmann
34,124
, Costanza Ludovica Vallerga
29
,
Dennis van der Meer
8,9
, Marjolein M.J. van Donkelaar
3,4
, Liza van Eijk
125,31
, Theo G.M. van Erp
85
, Neeltje E.M.
van Haren
22,126
, Daan van Rooij
86,4
, Marie-José van Tol
127
, Jan H. Veldink
128
, Ellen Verhoef
109
, Esther
Walton
122,129
, Yunpeng Wang
8,9
, Joanna M. Wardlaw
50,84,130
, Wei Wen
119
, Lars T. Westlye
8,9,105
, Christopher D.
Whelan
2,12
, Stephanie H. Witt
131
, Katharina Wittfeld
132,57
, Christiane Wolf
133
, Thomas Wolfers
3
, Clarissa L.
Yasuda
134,82
, Dario Zaremba
24
, Zuo Zhang
135
, Alyssa H. Zhu
2
, Marcel P. Zwiers
86,117,4
, Eric Artiges
136
, Amelia
A. Assareh
119
, Rosa Ayesa-Arriola
137,121
, Aysenil Belger
52
, Christine L. Brandt
8,9
, Gregory G. Brown
138
, Sven
Cichon
94,63,104
, Joanne E. Curran
14
, Gareth E. Davies
139
, Franziska Degenhardt
140
, Bruno Dietsche
141
, Srdjan
Djurovic
142,48
, Colin P. Doherty
143,144,145
, Ryan Espiritu
146
, Daniel Garijo
146
, Yolanda Gil
146
, Penny A.
Gowland
147
, Robert C. Green
148,149,150
, Alexander N. Häusler
151,152
, Walter Heindel
153
, Beng-Choon Ho
154
,
Wolfgang U. Hoffmann
118,132
, Florian Holsboer
155,55
, Georg Homuth
156
, Norbert Hosten
157
, Clifford R. Jack
Jr.
158
, MiHyun Jang
146
, Andreas Jansen
141,159
, Knut Kolskår
8,9,105,106
, Sanne Koops
22
, Axel Krug
141
, Kelvin O.
Lim
160
, Jurjen J. Luykx
161,22,162
, Daniel H. Mathalon
163,164
, Karen A. Mather
119,113
, Venkata S. Mattay
28,165,166
,
Sarah Matthews
129
, Jaqueline Mayoral Van Son
137,121
, Sarah C. McEwen
167,168,169
, Ingrid Melle
8,9
, Derek W.
Morris
25
, Bryon A. Mueller
160
, Matthias Nauck
170,171
, Jan E. Nordvik
106
, Markus M. Nöthen
140
, Daniel S.
O'Leary
154
, Nils Opel
24
, Marie - Laure Paillère Martinot
136,172
, G. Bruce Pike
173
, Adrian Preda
174
, Erin B.
Quinlan
135
, Varun Ratnakar
146
, Simone Reppermund
119,175
, Vidar M. Steen
48,176
, Fábio R. Torres
35,82
, Dick J.
Veltman
92
, James T. Voyvodic
52
, Robert Whelan
177
, Tonya White
126,178
, Hidenaga Yamamori
179
, Hieab H.H.
Adams
180,181
, Joshua C. Bis
182
, Stephanie Debette
183,184
, Charles Decarli
185
, Myriam Fornage
186
, Vilmundur
Gudnason
187,188
, Edith Hofer
189,190
, M. Arfan Ikram
180
, Lenore Launer
191
, W. T. Longstreth
192
, Oscar L.
Lopez
180,193
, Bernard Mazoyer
194
, Thomas H. Mosley
195
, Gennady V. Roshchupkin
180,193,181
, Claudia L.
Satizabal
196,197,198
, Reinhold Schmidt
199
, Sudha Seshadri
196,198,,200
, Qiong Yang
201
, The Alzheimer's Disease
Neuroimaging Initiative#, CHARGE consortium#, EPIGEN consortium#, IMAGEN consortium#, SYS
consortium#, The Parkinson’s Progression Markers Initiative#, Marina K.M. Alvim
134,82
, David Ames
202,203
, Tim
J. Anderson
89,90,91,204
, Ole A. Andreassen
8,9
, Alejandro Arias-Vasquez
107,3,4
, Mark E. Bastin
50,84
, Bernhard T.
Baune
205
, John Blangero
14
, Dorret I. Boomsma
37
, Henry Brodaty
119,206
, Han G. Brunner
3,4,207
, Randy L.
Buckner
208,209,210
, Jan K. Buitelaar
86,4,211
, Juan R. Bustillo
212
, Wiepke Cahn
213
, Vince Calhoun
214,123
, Xavier
Caseras
79
, Svenja Caspers
215,63,65
, Gianpiero L. Cavalleri
216,217
, Fernando Cendes
134,82
, Benedicto Crespo-
Facorro
137,121
, John C. Dalrymple-Alford
218,90,91
, Udo Dannlowski
24
, Eco J.C. de Geus
37
, Ian J. Deary
84,83
,
Chantal Depondt
219
, Sylvane Desrivières
135,62
, Gary Donohoe
25
, Thomas Espeseth
105,8
, Guillén Fernández
86,4
,
Simon E. Fisher
109,4
, Herta Flor
220
, Andreas J. Forstner
140,221,94,104
, Clyde Francks
109,4
, Barbara Franke
3,107,4
,
David C. Glahn
87,88
, Randy L. Gollub
209,210,80
, Hans J. Grabe
132,57
, Oliver Gruber
77
, Asta K. Håberg
222,223
, Ahmad
R. Hariri
76
, Catharina A. Hartman
224
, Ryota Hashimoto
225,179,226
, Andreas Heinz
227
, Manon H.J. Hillegers
126,228
,
Pieter J. Hoekstra
224
, Avram J. Holmes
229,209
, L. Elliot Hong
230
, William D. Hopkins
231,232
, Hilleke E. Hulshoff
Pol
22
, Terry L. Jernigan
233,42,138,33
, Erik G. Jönsson
11,9
, René S. Kahn
234,22
, Martin A. Kennedy
103
, Tilo T.J.
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
2
Kircher
141
, Peter Kochunov
230
, John B.J. Kwok
235,114,113
, Stephanie Le Hellard
48,176
, Nicholas G. Martin
30
, Jean-
Luc Martinot
136
, Colm McDonald
25
, Katie L. McMahon
236
, Andreas Meyer-Lindenberg
237
, Rajendra A.
Morey
52,53
, Lars Nyberg
16,17,238
, Jaap Oosterlaan
239,240,241
, Roel A.. Ophoff
100
, Tomas Paus
242,243,244
, Zdenka
Pausova
21,245
, Brenda W.J.H. Penninx
92
, Tinca J.C. Polderman
58
, Danielle Posthuma
58,246
, Marcella
Rietschel
131
, Joshua L. Roffman
209
, Laura M. Rowland
230
, Perminder S. Sachdev
119,247
, Philipp G. Sämann
55
,
Gunter Schumann
135,62
, Kang Sim
248
, Sanjay M. Sisodiya
71,249
, Jordan W. Smoller
46,209,250
, Iris E.
Sommer
251,228,127,224
, Beate St Pourcain
129,109,4
, Dan J. Stein
34,252
, Arthur W. Toga
18
, Julian N. Trollor
175,119
, Nic
J.A. Van der Wee
253
, Dennis van 't Ent
37
, Henry Völzke
118
, Henrik Walter
41
, Bernd Weber
152,151
, Daniel R.
Weinberger
28,254
, Margaret J. Wright
31,255
, Juan Zhou
256
, Jason L. Stein
7
**, Paul M. Thompson
2
**, Sarah E.
Medland
1
**
1 Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
2 Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC,
University of Southern California, Los Angeles, USA.
3 Department of Human Genetics, Radboud university medical center, Nijmegen, The Netherlands.
4 Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
5 Neuroscience Biomarkers, Janssen Research and Development, LLC, San Diego, USA.
6 Graduate Interdepartmental Program in Neuroscience, University of California Los Angeles, Los Angeles, USA.
7 Department of Genetics & UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
8 NORMENT - K.G. Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo,
Norway.
9 NORMENT - K.G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
10 Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.
11 Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
12 Department of Molecular and Cellular Therapeutics, Royal College of Surgeons in Ireland, Dublin, Ireland.
13 Neurology Department, University of California at San Francisco, San Francisco, USA.
14 Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School
of Medicine, Brownsville, USA.
15 Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.
16 Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.
17 Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.
18 Laboratory of Neuro Imaging, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the
University of Southern California, Los Angeles, USA.
19 Mathematics and Statistics, Murdoch University, Murdoch, Australia.
20 Duke Molecular Physiology Institute, Duke University Medical Center, Durham, USA.
21 The Hospital for Sick Children, University of Toronto, Toronto, Canada.
22 Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The
Netherlands.
23 Institute for Radiology and Neuroradiology, University Medicine, Ernst-Moritz-Arndt University, Greifswald, Germany.
24 Department of Psychiatry, University of Münster, Münster, Germany.
25 Centre for Neuroimaging & Cognitive Genomics, National University of Ireland Galway , Galway, Ireland.
26 Douglas Mental Health University Institute, McGill University, Montreal, Canada.
27 Departments of Psychiatry and Biological and Biomedical Engineering, McGill University, Montreal, Canada.
28 Lieber Institute for Brain Development, Baltimore, USA.
29 Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
30 Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Australia.
31 Queensland Brain Institute, University of Queensland, St Lucia, Australia.
32 Department of Neurosciences, University of California, San Diego, La Jolla, USA.
33 Department of Radiology, University of California San Diego, San Diego, USA.
34 Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa.
35 Department of Medical Genetics, School of Medical Sciences, University of Campinas - UNICAMP, Campinas, Brazil.
36 Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, Australia.
37 Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
38 Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam
UMC, Amsterdam, The Netherlands.
39 Division of Psychological & Social Medicine and Developmental Neurosciences, Technische Universität Dresden, Dresden,
Germany.
40 Division of Human Genetics, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South
Africa.
41 Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité -
Universitätsmedizin Berlin corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute o
f
Health, Berlin, Germ
any.
42 Department of Cognitive Science, University of California San Diego, San Diego, USA.
43 Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
44 San Francisco Veterans Administration Medical Center, San Francisco, USA.
45 Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Japan.
46 Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston,
USA.
47 Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA.
48 NORMENT - K.G. Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Norway.
49 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, USA.
50 Centre for Clinical Brain Sciences and Edinburgh Imaging, University of Edinburgh, Edinburgh, UK.
51 Division of Psychiatry, University of Edinburgh, Edinburgh, UK.
52 Duke UNC Brain Imaging and Analysis Center, Duke University Medical Center, Durham, USA.
53 Mental Illness Research Education and Clinical Center for Post Deployment Mental Health, Durham VA Medical Center, Durham,
USA.
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
3
54 Department of Cognitive and Clinical Neuropsychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
55 Max Planck Institute of Psychiatry, Munich, Germany.
56 Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan.
57 Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany.
58 Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands.
59 Department of Neurology, Alzheimer Center, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Vrije Universiteit
Amsterdam, Amsterdam, The Netherlands.
60 Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
61 Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education,
Shanghai, China.
62 Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience,
King's College London, London, UK.
63 Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
64 Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.
65 JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.
66 Department of Neuroinformatics, Araya, Inc., Tokyo, Japan.
67 Sackler Centre for Consciousness Science, School of Psychology, University of Sussex, Falmer, UK.
68 Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan.
69 Department of Psychiatry, McGill University, Montreal, Canada.
70 McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada.
71 Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.
72 Public Psychiatry Division, Massachusetts Mental Health Center, Beth Israel Deaconess Medical Center, Harvard Medical School,
Boston, USA.
73 Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School,
Boston, USA.
74 Department of Genome Informatics, Graduate School of Medicine, Osaka University, Suita, Japan.
75 Department of Medical Biometry, Informatics and Epidemiology, University Hospital Bonn, Germany.
76 Department of Psychology and Neuroscience, Duke University, Durham, USA.
77 Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University Hospital,
Heidelberg, Germany.
78 Centre for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry & Psychotherapy,
University Medical Center Göttingen, Göttingen, Germany.
79 MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
80 Department of Psychiatry, Harvard Medical School, Boston, USA.
81 McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada.
82 BRAINN - Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil.
83 Department of Psychology, University of Edinburgh, Edinburgh, UK.
84 Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK.
85 Department of Psychiatry and Human Behavior, School of Medicine University of California, Irvine, Irvine, USA.
86 Department of Cognitive Neuroscience, Radboud university medical center, Nijmegen, The Netherlands.
87 Department of Psychiatry, Yale University School of Medicine, New Haven, USA.
88 Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, USA.
89 Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand.
90 New Zealand Brain Research Institute, Christchurch, New Zealand.
91 Brain Research New Zealand - Rangahau Roro Aotearoa, Christchurch, New Zealand.
92 Amsterdam UMC, Vrije Universiteit, Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience and GGZinGeest,
Amsterdam, The Netherlands.
93 IC - Institute of Computing, Campinas, Brazil.
94 Department of Biomedicine, University of Basel, Basel, Switzerland.
95 Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany.
96 Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.
97 Institute of Translational Medicine, Liverpool, United Kingdom.
98 Drug Discovery Research, Astellas Pharmaceuticals, 21 Miyukigaoka, Tsukuba, Ibaraki 305-8585, Japan.
99 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, USA.
100 Center for Neurobehavioral Genetics, University of California Los Angeles, Los Angeles, USA.
101 NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Y
vette, France.
102 Biostatistics and Computational Biology Unit, University of Otago. Christchurch, Christchurch, New Zealand.
103 Department of Pathology and Biomedical Science, University of Otago, Christchurch, Christchurch, New Zealand.
104 Institute of Medical Genetics and Pathology, University Hospital Basel, Basel, Switzerland.
105 Department of Psychology, University of Oslo, Oslo, Norway.
106 Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
107 Department of Psychiatry, Radboud university medical center, Nijmegen, The Netherlands.
108 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA.
109 Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
110 Orygen, The National Centre of Excellence for Youth Mental Health, Melbourne, Australia.
111 The Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia.
112 Department of Psychiatry, Vrije Universiteit University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The
Netherlands.
113 Neuroscience Research Australia, Sydney, Australia.
114 School of Medical Sciences, University of New South Wales, Sydney, Australia.
115 Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA.
116 Population Neuroscience & Developmental Neuroimaging, Bloorview Research Institute, University of Toronto, East York,
Canada.
117 Donders Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands.
118 Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
119 Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia.
120 Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Santander, Spain.
121 Centro Investigacion Biomedica en Red Salud Mental, Santander, Spain.
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
4
122 Department of Psychology, Georgia State University, Atlanta, USA.
123 Mind Research Network, Albuquerque, USA.
124 Department of Psychiatry, University of Vermont, Burlington, USA.
125 School of Psychology, University of Queensland, Brisbane, Australia.
126 Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center-Sophia Children’s Hospital, Rotterdam,
The Netherlands.
127 Cognitive Neuroscience Center, Department of Neuroscience, University Medical Center Groningen, Groningen, The Netherlands.
128 Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The
Netherlands.
129 MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, Bristol, UK.
130 UK Dementia Research Institute, The University of Edinburgh, Edinburgh, UK.
131 Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg
University, Mannheim, Germany.
132 German Center for Neurodegenerative Diseases Rostock/Greifswald, Greifswald, Germany.
133 Department of Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany.
134 Department of Neurology, FCM, UNICAMP, Campinas, Brazil.
135 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College
London, London, UK.
136 INSERM Unit 1000 - Neuroimaging & Psychiatry, Paris Saclay University,Gif sur Yvette, France.
137 Department of Psychiatry, University Hospital Marqués de Valdecilla, School of Medicine, University of Cantabria–IDIVAL,
Santander, Spain.
138 Department of Psychiatry, University of California San Diego, San Diego, USA.
139 Avera Institute for Human Genetics, Sioux Falls, USA.
140 Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany.
141 Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany.
142 Department of Medical Genetics, Oslo University Hospital, Oslo, Norway.
143 Department of Neurology, St James's Hospital, Dublin, Ireland.
144 Academic Unit of Neurology, TBSI, Dublin, Ireland.
145 Future Neuro, Royal College of Surgeons in Ireland, Dublin, Ireland.
146 Information Sciences Institute, University of Southern California, Los Angeles, USA.
147 Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK.
148 Brigham and Women's Hospital, Boston, USA.
149 The Broad Institute, Boston, USA.
150 Harvard Medical School , Boston, USA.
151 Center for Economics and Neuroscience, University of Bonn, Bonn, Germany.
152 Institute of Experimental Epileptology and Cognition Research, University Hospital Bonn, Germany.
153 Department of Clinical Radiology, University of Münster, Münster, Germany.
154 Department of Psychiatry, University of Iowa College of Medicine, Iowa City, USA.
155 HMNC Holding GmbH, Munich, Germany.
156 University Medicine Greifswald, Interfaculty Institute for Genetics and Functional Genomics, Department of Functional Genomics,
Greifswald, Germany.
157 Institute of Diagnostic Radiology and Neuroradiology, Greifswald, Germany.
158 Dept of Radiology, Mayo Clinic, Rochester, USA.
159 Core-Unit Brainimaging, Faculty of Medicine, University of Marburg, Marburg, Germany.
160 Department of Psychiatry, University of Minnesota, Minneapolis, USA.
161 Department of Translational Neuroscience, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University,
Utrecht, The Netherlands.
162 Department of Psychiatry, ZNA hospitals, Antwerp, Belgium.
163 Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, USA.
164 Mental Health Service 116d, Veterans Affairs San Francisco Healthcare System, San Francisco, USA.
165 Department of Neurology, Johns Hopkins University, Baltimore, USA.
166 Department of Radiology, Johns Hopkins University, Baltimore, USA.
167 Department of Psychiatry, University of California San Diego, La Jolla, USA.
168 Pacific Brain Health Center, Santa Monica, United States.
169 John Wayne Cancer Institute, Santa Monica, United States.
170 Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.
171 German Centre for Cardiovascular Research, Greifswald, Germany.
172 Child and adolescent psychiatry department, APHP Pitié Salpêtrière hospital,Paris,France.
173 Radiology and Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, Canada.
174 School of Medicine, University of California Irvine, Irvine, USA.
175 Department of Developmental Disability Neuropsychiatry, University of New South Wales, Sydney, Australia.
176 Dr. Einar Martens Research Group for Biological Psychiatry, Center for
Medical Genetics and Molecular Medicine, Haukeland
University Hospital, Bergen, Norway.
177 School of Psychology, Trinity College Dublin, Dublin, Ireland.
178 Department of Radiology, Erasmus University Medical Centre, Rotterdam, The Netherlands.
179 Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan.
180 Department of Epidemiology, Erasmus MC Medical Center, Rotterdam, The Netherlands.
181 Department of Radiology and Nuclear Medicine, Erasmus MC Medical Center, Rotterdam, The Netherlands.
182 Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, USA.
183 team VINTAGE, University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Bordeaux, France.
184 Department of Neurology, CHU de Bordeaux, Bordeaux, Franc.
185 Department of Neurology, University of California, Davis, Sacramento, USA.
186 Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, USA.
187 Icelandic Heart Association, Kopavogur, Iceland.
188 Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
189 Clinical Division of Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria.
190 Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.
191 Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, USA.
192 Departments of Neurology and Epidemiology, University of Washington, Seattle, USA.
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5
193 Medical Informatics, Erasmus MC Medical Center, Rotterdam, The Netherlands.
194 Neurodegeneratives Diseases Institute-CNRS UMR 5293, University of Bordeaux, France;.
195 MIND Center, University of Mississippi Medical Center, Jackson, USA.
196 Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio,
USA.
197 Department of Epidemiology & Biostatistics, University of Texas Health Sciences Center, San Antonio, USA.
198 Department of Neurology, Boston University School of Medicine, Boston, USA.
199 Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, USA.
200 Framingham Heart Study and Department of Neurology, Boston University School of Medicine, Boston, USA.
201 Department of Biostatistics, Boston University School of Public Health, Boston, USA.
202 Academic Unit for Psychiatry of Old Age, University of Melbourne, Melbourne, Australia.
203 National Ageing Research Institute, Melbourne, Australia.
204 Department of Neurology, Canterbury District Health Board, Christchurch, New Zealand.
205 Department of Psychiatry, The University of Melbourne, Melbourne, Australia.
206 Dementia Centre for Research Collaboration, University of New South Wales, Sydney, Australia.
207 Department of Clinical Genetics and School for Oncology & Developmental Biology (GROW), Maastricht University Medical
Center, Maastricht, The Netherlands.
208 Department of Psychology and Center for Brain Science, Harvard University, Boston, USA.
209 Department of Psychiatry, Massachusetts General Hospital, Boston, USA.
210 Department of Radiology, Massachusetts General Hospital , Boston, USA.
211 Karakter Child and Adolescent Psychiatry University Center, Nijmegen, The Netherlands.
212 Department of Psychiatry, University of New Mexico, Albuquerque, USA.
213 Department of Psychiatry, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
214 Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, USA.
215 Institute for Anatomy I, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
216 Molecular and Cellular Therapeutics, The Royal College of Surgeons In Ireland, Dublin, Ireland.
217 The SFI FutureNeuro Research Centre, Dublin, Ireland.
218 Department of Psychology, University of Canterbury, Christchurch, New Zealand.
219 Department of Neurology, Hôpital Erasme, Université Libre de Bruxelles, Brussels, Belgium.
220 Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg
University, Mannheim, Germany.
221 Department of Psychiatry (UPK), University of Basel, Basel, Switzerland.
222 Department of Neuroscience, Norwegian University of Science and Technology, Trondheim, Norway.
223 Department of Radiology and Nuclear medicine, Trondheim, Norway.
224 Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
225 Molecular Research Center for Children’s Mental Development, United Graduate School of Child Development, Osaka University,
Suita, Japan.
226 Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry,
Tokyo, Japan.
227 Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité - Universitätsmedizin Berlin corporate member of
Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.
228 Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
229 Department of Psychology, Yale University, New Haven, USA.
230 Maryland Psychiatry Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, USA.
231 Neuroscience Institute, Georgia State University, Atlanta, USA.
232 Division of Developmental and Cognitive Neuroscience, Yerkes National Primate Research Center, Atlanta, USA.
233 Center for Human Development, University of California San Diego, La Jolla, USA.
234 Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, USA.
235 Neurogenetics and Epigenetics, Brain and Mind Centre, The University of Sydney, Sydney, Australia.
236 Herston Imaging Research Facility, School of Clinical Sciences, Queensland University of Technology, Brisbane, Australia.
237 Department of Psychiatry and Psychotherapy, Central Institute of Mental Health Mannheim, Medical Faculty Mannheim,
Heidelberg University, Mannheim, Germany.
238 Department of Radiation Sciences, Umeå University, Umeå, Sweden.
239 Emma Children's Hospital Academic Medical Center, Amsterdam, The Netherlands.
240 Department of Pediatrics, Vrije Universiteit Medical Center, Vrije U
niversiteit Amsterdam, Amsterdam, The Netherlands.
241 Clinical Neuropsychology section, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
242 Bloorview Research Institute, University of Toronto, Toronto, Canada.
243 Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada.
244 Centre for Developing Brain, Child Mind Institute, New York City, USA.
245 Department of Physiology, University of Toronto, Toronto, Canada.
246 Department of Clinical Genetics, Vrije Universiteit Medical Centre, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
247 Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, Australia.
248 General Psychiatry, Institute of Mental Health, Singapore, Singapore.
249 Chalfont Centre for Epilepsy, Chalfont-St-Peter, UK.
250 Stanley Center for Psychiatric Research, Broad Institute, Boston, USA.
251 Department of Medical and Biological Psychology, University of Bergen, Bergen, Norway.
252 MRC Unit on Risk & Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa.
253 Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands.
254 Psychiatry, Neurology, Neuroscience, Genetics, Johns Hopkins University, Baltimore, USA.
255 Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.
256 Center for Cognitive Neuroscience, Neuroscience and behavioral disorders program, Duke-National University of Singapore
Medical School, Singapore, Singapore.
Authorship contributions (*
,
**) and authorship lists for the consortium authors (
#
) appear in the Supplementary Notes.
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Summary
The cerebral cortex underlies our complex cognitive capabilities, yet we know little about the specific genetic
loci influencing human cortical structure. To identify genetic variants impacting cortical structure, we conducted
a genome-wide association meta-analysis of brain MRI data from 35,660 individuals with replication in 15,578
individuals. We analysed the surface area and average thickness of the whole cortex and 34 regions with
known functional specialisations. We identified 206 nominally significant loci (P ≤ 5 x 10
-8
); 150 survived
multiple testing correction (P ≤ 8.3 x 10
-10
; 140 surface area; 10 thickness). We found significant enrichment
for loci influencing total surface area within regulatory elements active during prenatal cortical development,
supporting the radial unit hypothesis. Loci impacting regional surface area cluster near genes in Wnt signalling
pathways, known to influence progenitor expansion and areal identity. Variation in cortical structure is
genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression and ADHD.
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The human cerebral cortex is the outer grey matter layer of the brain, which is implicated in multiple aspects
of higher cognitive function. Its distinct folding pattern is characterised by convex (gyral) and concave (sulcal)
regions. Computational brain mapping approaches use the consistent folding patterns across individual
cortices to label brain regions
1
(Fig. 1a). During fetal development excitatory neurons, the predominant
neuronal cell-type in the cortex, are generated from neural progenitor cells in the developing germinal zone
2
.
The radial unit hypothesis
3
posits that the expansion of cortical surface area (SA) is driven by the proliferation
of these neural progenitor cells, whereas thickness (TH) is determined by the number of neurogenic divisions.
Variation in global and regional measures of cortical SA and TH are associated with neuropsychiatric disorders
and psychological traits
4-6
(Supplementary Table 1). Twin and family-based brain imaging studies show that
SA and TH measurements are highly heritable and are largely influenced by independent genetic factors
7,8
.
Despite extensive studies of genes impacting cortical structure in model organisms
9
, our current understanding
of genetic variation impacting human cortical size and patterning is limited to rare, highly penetrant variants
10,11
.
These variants often disrupt cortical development, leading to altered post-natal structure. However, little is
known about how common genetic variants impact human cortical SA and TH.
To address this, we conducted genome-wide association meta-analyses of cortical SA and TH measures in
51,238 individuals from 58 cohorts from around the world (Supplementary Fig. 1; Supplementary Tables 2–4).
Cortical measures were extracted from structural brain MRI scans in regions defined by gyral anatomy using
the Desikan-Killiany atlas
12
. Image processing and quality control are described in the Methods. We analysed
two global measures, total SA and average TH, and SA and TH for 34 regions averaged across both
hemispheres, yielding 70 distinct phenotypes (Fig. 1a; Supplementary Table 1).
Within each cohort genome-wide association (GWAS) for each of the 70 phenotypes was conducted using an
additive model (Methods). To identify genetic influences specific to each region, the primary GWAS of regional
measures included the global measure of SA or TH as a covariate. To better localise the global findings,
regional GWAS were also run without controlling for global measures. To estimate the multiple testing burden
associated with analysing 70 phenotypes, we used matrix spectral decomposition
13
, which yielded 60
independent traits (Methods). Therefore, we adopted a significance threshold of P ≤ 8.3 x 10
-10
.
A rolling genome-wide meta-analytic approach was used with three phases (Methods; Supplementary Fig. 1).
Initial meta-analysis comprised results from 34 ENIGMA cohorts of European ancestry (19,512 participants)
and the UK Biobank
14
(10,083 participants of European ancestry; Methods). The second phase included ten
additional ENIGMA cohorts of European ancestry (3,121 participants) submitted after the first meta-analysis.
Results of this second phase of meta-analysis were used in all follow-up analyses. The third phase included
results from eight ENIGMA cohorts of non-European ancestry (2,944 participants). We sought further
replication from participants of European ancestry for loci reaching P ≤ 5 x 10
-8
in four additional ENIGMA
cohorts (1,628 participants) and with the CHARGE consortium (as a reciprocal replication; 13,950 participants,
excluding UK Biobank). High genetic correlations were observed between the meta-analysed ENIGMA
European cohorts (excluding UK Biobank) and the UK Biobank cohort using LD-score regression (rG
TotalSA
=
1.00, P = 2.4 x 10
-26
, rG
AverageTH
= 0.94, P = 2.5 x 10
-19
) indicating consistent genetic architecture between these
34 ENIGMA cohorts and the single-site, single-scanner UK Biobank cohort.
Across the 70 cortical phenotypes, we identified 213 loci that were nominally genome-wide significant in the
second phase of meta-analysis (P ≤ 5 x 10
-8
). Including the non-European cohorts in the meta-analysis yielded
an additional 38 loci meeting this threshold, resulting in a total of 251 loci. After including the replication data,
206 loci remained nominally significant (188 influencing SA and 18 influencing TH); 150 of these survived
multiple testing (P ≤ 8.3 x 10
-10
; 140 influencing SA and 10 influencing TH; Fig. 1b; Supplementary Table 1;
Supplementary Table 5). Significant gene-based association was observed for 479 genes across the 70
cortical phenotypes (Methods; Supplementary Table 6). Figures summarising the meta-analytic results
(Manhattan, QQ and Forest plots) are provided in the supplementary materials.
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Fig. 1 | Regions of the human cortex and associated genetic loci. a, the 34 cortical regions defined by the
Desikan-Killiany atlas
12
; b, ideogram of nominal (P ≤ 5 x 10
-8
) and genome-wide significant loci influencing
cortical SA and TH; and c, number of genome-wide significant (P ≤ 8.3 x 10
-10
) loci influencing cortical SA and
TH.
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Genetic architecture of total SA and average TH
Common variants explained 33% (SE = 3%) of the variation in total SA and 25% (SE = 2%) in average TH,
which approaches a third of the heritability estimated from twin and family studies
8
(Methods; Supplementary
Table 1; Supplementary Table 7). We observed a significant negative genetic correlation between total SA and
average TH (r
G
= -.32, SE = .05, P = 3.9 x 10
-11
; Fig. 2a), which persisted after excluding the chromosome 17
inversion region known to influence brain size
15-17
(r
G
= -.31, SE = .05, P = 4.7 x 10
-11
). The direction of this
correlation suggests that opposing genetic influences may constrain the total cortical size. The small
magnitude of this correlation is consistent with the radial unit hypothesis
10
whereby different developmental
mechanisms promote SA and TH expansion.
As expected, total SA showed a positive genetic correlation with intracranial volume (ICV); this correlation
remained after controlling for height demonstrating that this relationship is not solely driven by body size (Fig.
2a; Supplementary Table 8). The global cortical measures did not show significant genetic correlations with
the volumes of major subcortical structures (Fig. 2a), indicating that variation in cortical and subcortical
structures are have predominantly independent genetic influences. This is consistent with known differences
in cell-type composition between these structures.
To identify if common variation associated with cortical structure perturbs gene regulation during a specific
developmental time period or within a given cell-type, we performed partitioned heritability analyses
18
using
sets of gene regulatory annotations from adult and fetal brain tissues
19,20
. The strongest enrichment of the
heritability for global SA was seen within areas of active gene regulation (promoters and enhancers) in the
mid-fetal human brain (Methods; Fig. 2b). We further identified a stronger enrichment in regions of the fetal
cortex with more accessible chromatin in the neural progenitor-enriched germinal zone than the neuron-
enriched cortical plate
19
. There was also enrichment of active regulatory elements within embryonic stem cells
differentiated to neural progenitors
20
. We conducted pathway analyses to determine if there was enrichment
of association near genes in known biological pathways (Methods). Among the 241 significant gene-sets there
a number were involved in chromatin modification, a process guiding neurodevelopmental fate decisions
21
(Fig. 3c, Supplementary Table 9). These findings suggest that total SA in adults is influenced by common
genetic variants that may alter gene regulatory activity in neural progenitor cells during fetal development,
supporting the radial unit hypothesis
3
. The strongest evidence of enrichment for average TH was found in
active regulatory elements in the adult brain samples, which may reflect processes occurring after mid-fetal
development, such as myelination, branching, and pruning
22
. These findings are consistent with the radial unit
hypothesis, which proposes that neocortical surface area expansion is largely driven by increases in the neural
progenitor pool
3
.
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Fig. 2 | Genetics of Global Cortical Measures. a, Genetic correlations between global measures and
selected morphological traits (β, SE and P values are reported in full in Supplementary Table 8); positive
correlations are shown in red, negative correlations are shown in blue; b, Partitioned heritability; c, Miami plot
shows loci associated with global measures (top: surface area, bottom: thickness), green highlights are the
loci that reach nominal genome-wide significance in either Phase 2 or Phase 3, the black dashed to black
diamonds indicate change in P-value of the lead SNP after replication; d, Regional plot for rs1628768; e, Effect
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of rs1628768 (C allele) on the SA of cortical regions without controlling for global measures; f, Regional plot
for rs630934; g, Effect of rs630934 (A allele) on the TH of cortical regions without controlling for global
measures. Within the regional plots the three panels contain: i) proxy SNPs and surrounding genes; ii)
Chromatin state in four RoadMap brain tissues: dorsolateral prefrontal cortex (DPfC), fetal brain (female, Fet-
F, and male, Fet-M) and NH-A_astrocytes_primary_cells (NH-A APC). iii) BRAINSPAN gene expression in
fetal and adult brain tissue (Supplementary Note).
Loci influencing total SA and average TH
Eleven loci were nominally associated with total SA; eight survived correction for multiple testing (Fig. 2c,
Supplementary Table 5). While these loci were significantly associated with global measures the effects were
not uniform across regions (Fig. 2e; 2g). Five loci influencing total SA have been previously associated with
ICV
16
(Fig. 2c). Of these, rs62057153 (P
phase2
= 2.7 x 10
-30
; P
rep
= 6.3 x 10
-42
), in the highly pleiotropic
chromosome 17q21.31 inversion region
15-17
has previously been associated with Parkinson’s disease
23
,
educational attainment
24
, and neuroticism
25
(Supplementary Fig. 2a). On 10q24.33, rs1628768 (P
phase2
= 3.8
x 10
-13
; P
rep
= 5.3 x 10
-18
) is a cortical expression quantitative trait locus (eQTL)
26
for WBP1L, INA, and the
putative schizophrenia genes AS3MT and NT5C2
27
(CommonMind Consortium [CMC] FDR P = 0.009; Fig.
2d; Supplementary Table 10-11; Methods). This region has been associated with schizophrenia; however,
rs1628768 is in low LD with the schizophrenia-associated SNP, rs11191419 (r
2
= 0.15). The 6q21 locus
influencing total SA is intronic to FOXO3 (which also showed a significant gene-based association with total
SA, Supplementary Table 6). The minor allele of the lead variant rs2802295 is associated with decreased total
SA (P
phase2
= 2.3 x 10
-10
; P
rep
= 9.2 x 10
-14
) and has previously been associated with lower general cognitive
function
28
(rs2490272: P
Cognition
= 9.9 x 10
-14
; r
2
rs2802295:rs2490272
= 1, Supplementary Fig. 2b). The three loci
influencing total SA not previously associated with ICV were rs34464850 (P
phase2
= 1.7 x 10
-16
; P
rep
= 2.6 x 10
-
17
) in proximity to genes TFDP2 and ATP1B3, rs11171739 (P
phase2
= 8.2 x 10
-9
; P
rep
= 2.2 x 10
-10
) located
between RPS26 and ERBB3, and rs190958130 (P
phase2
= 6.2 x 10
-11
; P
rep
= 3.3 x 10
-11
) near CENPW
(Supplementary Note).
Among the nominally significant results, the 3p24.1 locus (rs12630663; P
phase2
= 2.0 x 10
-8
; P
rep
= 9.7 x 10
-9
) is
of interest due to its proximity (~200kb) to EOMES (also known as TBR2), which is expressed specifically in
intermediate progenitor cells
29
, in the developing fetal cortex
2
. rs12630663 is located in a chromosomal region
with chromatin accessibility specific to the human fetal cortex germinal zone of human
19
. This region shows
significant chromatin interaction with the EOMES promoter
29
and contains numerous regulatory elements that
when excised via CRISPR/Cas9 in differentiating neural progenitor cells significantly reduced EOMES
expression
19
. A rare homozygous chromosomal translocation in the region separating the regulatory elements
from EOMES silences its expression and causes microcephaly
30
(Supplementary Fig. 5).
Four loci were nominally associated with average TH; only one survived correction for multiple testing (Fig. 2c;
Supplementary Table 5). The chromosome 3p22.1 locus (rs630934; P
phase2
= 3.4 x 10
-10
; P
rep
= 9.4 x 10
-12
) is
located between RPSA (encoding a 40S ribosomal protein with a potential role as a laminin receptor
31
) and
MOBP (involved in myelination and differentiation of oligodendrocytes
31
). rs630934 is an eQTL for MOBP in
tibial nerve (P
GTEx
= 1.17 x 10
-13
) and for RPSA in multiple tissues including cerebellum (P
GTEx_cerebellum
= 5.4 x
10
-6
; Fig. 2f; Supplementary Table 10-11). Among the nominally significant results, the 2q11.2 locus (P
phase2
=
2.1 x 10
-9
; P
rep
= 2.1 x 10
-9
) is of particular interest because rs11692435 is a missense variant (p.A143V)
predicted to impact ACTR1B function (Supplementary Table 10-11). ACTR1B is expressed in numerous tissue
types and is a component of the dynactin complex, necessary for vesicle movement along microtubules
32
.
Genetic architecture of regional SA and TH
Within individual cortical regions the amount of phenotypic variance explained by common variants was higher
for SA (9-31%) than for TH (0.5-16%) (Methods; Fig. 3a-b; Supplementary Table 1; Supplementary Table 7).
With few exceptions, the genetic correlations between SA and TH within the same region were moderate and
negative (Supplementary Table 12-13), suggesting that genetic variants contributing to the expansion of SA
tend to decrease TH. Most genetic correlations between regional surface areas did not survive multiple testing
correction, and those that did implied a general pattern of positive correlations between physically adjacent
regions and negative correlations with more distal regions (Fig. 3a). This pattern mirrored the phenotypic
correlations between regions and was also observed for TH (Fig. 3a-b). The positive genetic correlations were
typically between SA of regions surrounding the major, early forming sulci (e.g., pericalcarine, lingual, cuneus,
and lateral occipital regions surrounding the calcarine sulcus), which may potentially reflect genetic effects
acting on the development of the sulci (see Supplementary Note for further discussion). However, the general
pattern of correlations may, in part, depend on the regional partitioning by the Desikan-Killiany atlas
12
(see
Supplementary Note for further discussion). Hierarchical clustering of the genetic correlations resulted in a
general grouping by physical proximity, with a well-defined cluster in the occipital lobe (Methods;
Supplementary Fig. 3).
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To further investigate biological pathways influencing areal identity, we summarised the individual regional
results using multivariate GWAS analyses
33
separately for SA and TH that modelled the phenotypic
correlations between regions (Methods). Pathway analyses of the multivariate SA results showed significant
enrichment for 493 gene sets (Fig. 3c-d; Supplementary Table 9), many of which are involved in Wnt signalling,
with the canonical Wnt signalling pathway showing the strongest enrichment (P = 3.9 x 10
-7
). Wnt proteins
regulate neural progenitor fate decisions
34,35
and are expressed in spatially specific manners influencing areal
identity
9
. Pathway analyses of the multivariate TH results did not yield any findings that survived multiple
testing.
Fig. 3 | Genetic and Phenotypic Correlations between cortical regions. a, Surface Area; b, Thickness.
The regions are referred to by the numbers shown in the legend of Fig. 1a. The proportion of variance
accounted for by common genetic variants is shown in the first column (h
2
SNP
). Phenotypic correlations for the
UK Biobank are shown in upper triangle while genetic correlations from the second phase of meta-analysis
are shown in the lower triangle. All analyses are corrected for the covariates included in the GWAS (Methods).
c, Enrichment of gene ontology annotations for Total Surface Area; d, Enrichment of gene ontology
annotations for regional surface area.
Loci influencing regional SA and TH
A total of 177 loci were nominally associated with regional SA and 14 with TH; of these 132 SA and 9 TH loci
survived multiple testing correction (Supplementary Table 1; Supplementary Table 5). As shown in Fig. 1b,
most loci identified were associated with a single cortical region. Of the loci influencing regional measures, few
were associated with global measures, and those that were showed effects in the same direction, implying that
the significant regional loci were not due to collider bias
36
(Supplementary Fig. 4).
The strongest regional association was observed on chromosome 15q14 with the precentral SA (rs1080066,
P
phase2
= 6.9 x 10
-132
; P
rep
= 4.4 x 10
-188
; variance explained = 0.87% Fig. 4a-b). Across traits within the 15q14
region we observed 14 independent significant associations from six LD blocks (r2 threshold <=.4; see Fig.
4b, Supplementary Table 1; Supplementary Table 5). As we observed strong association with the SA of both
pre- and post-central gyri, we localised the association within the central sulcus in 5,993 unrelated individuals
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13
from the UK Biobank (Methods). The maximal association between rs1080066 and sulcal depth was observed
around the pli de passage fronto-pariétal moyen (P = 7.9 x 10
-21
), a region associated with hand fine-motor
function in humans
37
and shows distinct depth patterns across different species of primates
38
(see Fig. 4d).
Located in a large intergenic region rs1080066 is an eQTL for the downstream gene THBS1 in whole blood
(P
BIOS_genelevel
= 1.5 x 10
-9
) with evidence of chromatin interaction between the rs1080066 region and the THBS1
promoter in neural progenitor cells (Fig. 4a).
Across the 14q23.1 region, we observed 14 significant associations from three loci (Fig. 4e-f; Supplementary
Table 1; Supplementary Table 5). Within this region, our strongest association was observed with the
precuneus SA (rs73313052: P
phase2
= 1.7 x 10
-23
; P
rep
= 2.5 x 10
-35
; variance explained = 0.28%). rs73313052
is located between DACT1 and DAAM1, both of which are involved in synapse formation and are critical
members of the Wnt signalling cascade
39,40
. rs73313052 is an eQTL for DAAM1 in adult cortex (FDR P
CMC_SVA
= 0.009), with high LD proxies located within an active transcription start site in adult cortex and fetal brain
tissue (Fig. 4e; Supplementary Table 10-11). Consistent with enrichment in the pathway analyses, a number
of other loci were located in regions with functional links to genes involved in Wnt signalling, including 1p13.2,
where rs910697 (lingual SA, P
phase2
= 5.0 x 10
-11
; P
rep
= 1.1 x 10
-11
; a synonymous SNP in WNT2B exon 4) and
rs2999158 (pericalcarine SA, P
phase2
= 2.0 x 10
-12
; P
rep
= 1.1 x 10
-15
) are cortical eQTLs for ST7L and WNT2B
(FDR P
CMC_SVA
= 0.009; Supplementary Table 10–11).
A number of other regional associations occur near genes with known roles in brain development. For example,
on chromosome 1p22.2, rs1413536 (inferior parietal SA: P
phase2
= 9.7 x 10
-13
; P
rep
= 2.8 x 10
-14
) is a cortical
eQTL for LMO4 (FDR P
CMC_SVA
= 0.049). There are chromatin interactions between the region housing both
this SNP and rs11161942 (posterior cingulate SA: P
phase2
= 2.8 x 10
-10
; P
rep
= 4.4 x 10
-10
) and the LMO4
promoter in neural progenitor cells (Supplementary Table 10-11). Lmo4 is one of the few genes already known
to be involved in areal identity specification in mammalian brain
41
.
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14
Fig. 4 | Genetics of Regional Measures. a, Regional plot for rs1080066; b, Association of rs1080066 (G
allele) with SA of cortical regions; c, Sun plot showing brain regions associated with 15q14, colours indicate
different LD blocks; d, Association of rs1080066 with the depth of the central sulcus, and comparison of central
sulcus depth across humans and other primate species; e, Regional plot for rs73313052; f, Association of
rs73313052 (A allele) with SA of cortical regions; g, Sun plot showing brain regions associated with 14q23.1,
colours indicate different LD blocks; h, Regional plot for rs6505147. Within the regional plots the three panels
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15
contain: i) proxy SNPs and surrounding genes; ii) Chromatin state in four RoadMap brain tissues: dorsolateral
prefrontal cortex (DPfC), fetal brain (female, Fet-F, and male, Fet-M) and NH-A_astrocytes_primary_cells (NH-
A APC). iii) BRAINSPAN candidate gene expression in fetal and adult brain tissue (Supplementary Note).
Another locus of interest is chromosome 17q11.2 (Fig. 4g) where the lead variant, rs6505147 (P
phase2
= 4.0 x
10
-13
; P
rep
= 1.2 x 10
-12
), is associated with the insular SA. Located in EFCAB5, rs6505147 is centromeric to
the serotonin transporter SLC6A4. Both SLC6A4 and the insula have been implicated in mood disorders
(although support for SLC6A4 is equivocal)
42-44
. rs6505147 is in a large LD block that extends to SLC6A4, but
is not in LD (r
2
=.06) with the highly investigated 5-HTTLPR repeat polymorphism that is within SLC6A4 (see
Methods). rs6505147 is a cortical eQTLs in multiple databases for six genes flanking SLC6A4 (CORO6, SSH2,
EFCAB5, BLMH, GOSR1, SUZ12P), but not for SLC6A4 itself (Supplementary Table 10–11). While the LD
structure at this locus complicates the assignment of a candidate gene (Methods), increased EFCAB5
expression was recently associated with delayed brain aging, highlighting a possible role for this gene in the
human aging
45
.
Genetic relationships with neuropsychiatric disorders and psychological traits
To examine shared genetic effects between cortical structure and other traits, we performed genetic correlation
analyses with GWAS summary statistics from 24 selected traits (Methods). We observed significant positive
genetic correlations between total SA and general cognitive function
28
, educational attainment
24
, and
Parkinson’s disease
23
. For total SA, significant negative genetic correlations were detected with insomnia
46
,
attention deficit hyperactivity disorder
47
(ADHD), depressive symptoms
48
, major depressive disorder
49
, and
neuroticism
25
. Genetic correlations with average TH did not survive multiple testing correction due to the
weaker genetic association seen in the TH analyses (Fig. 5; Supplementary Table 14). We mapped genetic
correlation patterns across the cortical regions without correction for the global measures to map the
magnitude of these effects across the brain. No additional neuropsychiatric or psychological traits were
significant at a regional level.
Fig. 5 | Genetic correlations with neuropsychiatric and psychological traits. a, genetic correlations with
total SA and average TH positive correlations are shown in red, while negative correlations are shown in blue;
b, regional variation in the strength of genetic correlations between regional surface area (without correction
for total surface area) and traits showing significant genetic correlations with total surface area.
Discussion
Here we present a large-scale collaborative investigation of the effects of common genetic variation on human
cortical structure using data from 51,238 individuals from 58 cohorts from around the world. We identify specific
loci influencing cortical surface area (with 140 loci surviving multiple testing) and thickness (10 loci), implicating
genes involved in areal patterning and cortical development. Our results support the radial unit hypothesis of
surface area expansion in humans
3
: genetic variation within regulatory elements in fetal neural progenitor
cells
19
is associated with variability in adult cortical surface area. We also find that Wnt signalling genes
influence areal expansion in humans, as has been reported in model organisms such as mice
9
. Cortical
thickness was associated with loci near genes implicated in cell differentiation, migration, adhesion, and
myelination. Consequently, molecular studies in the appropriate tissues, such as neural progenitor cells and
their differentiated neurons, will be critical to map the involvement of specific genes. Genetic variation
associated with brain structure is functionally relevant, as evidenced by genetic correlations with a range of
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16
neuropsychiatric disorders and psychological traits, including general cognitive function, Parkinson’s disease,
depression, ADHD and insomnia. This work identifies novel genome-wide significant loci associated with
cortical surface area and thickness based on the largest imaging genetics study to date, providing a deeper
understanding of the genetic architecture of the human cerebral cortex and its patterning.
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18
Methods
Ethical approval and data availability
Participants in all cohorts in this study gave written informed consent and sites involved obtained approval
from local research ethics committees or Institutional Review Boards. Ethics approval for the meta-analysis
was granted by the QIMR Berghofer Medical Research Institute Human Research Ethics Committee (approval:
P2204). The meta-analytic results will be available to download from the ENIGMA consortium webpage upon
publication http://enigma.ini.usc.edu/research/download-enigma-gwas-results.
Imaging
Measures of cortical surface area (SA) and thickness (TH) were derived from in-vivo whole brain T1-weighted
magnetic resonance imaging (MRI) scans using FreeSurfer MRI processing software
1
(see Supplementary
Table 3). SA and TH were quantified for each subject within 34 distinct gyral-defined regions in each brain
hemisphere according to the Desikan-Killiany atlas
12
(Fig. 1a). SA was measured at the grey-white matter
boundary. TH was measured as the average distance between the white matter and pial surfaces. The total
SA and average TH of each hemisphere was computed separately. High test-retest correlations have been
reported for all measures with the exception of the frontal and temporal poles
8
. Image processing and quality
control were implemented at the cohort level following detailed, harmonized protocols (see
http://enigma.ini.usc.edu/protocols/imaging-protocols/ for protocols); phenotype distributions for all traits in all
cohorts were inspected centrally prior to meta-analysis any cohort where the phenotypic distribution for a given
trait showed deviation from expectations that could not be resolved through reanalysis or outlier inspection
were excluded from analyses of that trait.
Genome-wide association analyses
At each site, genotypes were imputed using either the 1000 Genomes Project
50
or Haplotype Reference
Consortium
51
references (Supplementary Table 4). To ensure consistency in the correction for ancestry and
stability of the correction given the relatively small sample sizes, each cohort also ran the same
multidimensional scaling (MDS) analysis protocol in which the data from the HapMap 3 populations were
merged with the site level data and MDS components were calculated across this combined data set. Within
each cohort, genome-wide association (GWAS) was conducted using an additive model including covariates
to control for the effects of age, sex, ancestry (the first four MDS components), diagnostic status (when the
cohort followed a case-control design), and scanner (when multiple scanners were used at the same site).
The primary GWAS of regional measures included the global measure of SA or TH as an additional covariate,
to test for genetic influences specific to each region. However, to aid interpretation, the regional GWAS were
also run without controlling for global measures. Cohort level GWAS results underwent quality control
(excluding variants with an imputation R
2
≤ .5 and MAF ≤ .005). Across all cohorts, for each phenotype, GWAS
summary plots (Manhattan and QQ plots) were visually inspected by the central analysis group, if a given trait
showed deviation from expectations that could not be resolved through reanalysis that cohort was excluded
from analyses of that trait. Given the large disparity in sample size (and corresponding fluctuation in power)
between indels and SNPs (see UK Biobank data below), we only carried forward SNPs within the meta-
analyses.
Multiple testing correction
We analysed 70 traits (total SA, average TH, and the SA and TH of 34 cortical regions averaged across right
and left). However, after accounting for the correlation between the traits in the UK Biobank (residuals
correcting for sex, age, ancestry and global measures) using matrix spectral decomposition (matSpD
13
) the
effective number of traits was estimated to be 60. Therefore, we applied the significance threshold of P ≤ 8.3
x 10
-10
to correct for multiple testing in the GWAS meta-analysis results. Multiple testing corrections applied to
each of the follow-up analyses are described below.
Meta-analysis
A rolling meta-analytic approach was used, where three phases of meta-analysis were conducted. The meta-
analytic workflow is described in Supplementary Fig. 1 and cohort information is provided in Supplementary
Table 2. All meta-analyses were conducted using METAL
52
. The results of the meta-analysis are summarized
in Supplementary Table 5. For meta-analyses phases 1-3 we used standard error weighted meta-analyses. In
the additional replication step, we used sample size weighted meta-analyses, in order to include results from
the CHARGE consortium for which only sample size weighted results were available. For each meta-analysis,
the results were quality controlled, removing strand ambiguous SNPs where the effect allele frequency crossed
.5, and variants where the total sample size was < 10,000 for phases 1-3.
We visually inspected the frequencies of effect alleles of all nominally genome-wide significant (P ≤ 5 x 10
-8
)
variants that were strand ambiguous. Three variants (rs10237366: lingual and pericalcarine SA, rs2269084:
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19
paracentral SA, rs4515470: superior temporal SA) showed allele frequency patterns that could not be
disambiguated for one or more of the non-European cohorts. In these cases, the data for the variant within the
cohort(s) that could not be resolved were dropped and the meta-analysis was re-run.
Following Rietveld et al
53
, we estimated the variance explained R
2
by each variant j as:
𝑅
2𝑝
𝑞
.𝛽
𝜎
where
p
j
and q
j
are the minor and major allele frequencies, 𝛽
is the estimated effect of the variant within the
meta-analysis and 𝜎
is the estimated variance of the trait (for which we used the pooled variance of the trait
across all ENIGMA cohorts and UK Biobank; see Supplementary Table 1). To obtain beta and standard error
estimates from the results from the sample size weighted meta-analyses reported in Supplementary Table 5
we used the following equations from Rietveld et al
53
:
𝛽
𝑧
∙
𝜎
𝑁
∙2𝑝
𝑞
and 𝑆𝐸𝛽
≡
𝑧
𝛽
Wherez
j
is the Z-score and SE(𝛽
) is the estimated standard effect of the variant within the meta-analysis and
N is the number of contributing alleles.
Analyses of UK Biobank data
Analyses of the UK Biobank cohort were conducted on the 2017 imputed genotypes restricted to variants
present in the Haplotype Reference Consortium
51
. UK Biobank bulk imaging data were made available for
12,962 individuals under application #11559 in July 2017. We processed the raw MRI data using the ENIGMA
protocols. Following processing, all images were visually inspected. Analyses of UK Biobank participants within
.02 on the first and second MDS components of the European centroid were included in the phase 1 meta-
analyses of the European ancestry cohorts (Supplementary Fig. 1). Analyses of participants beyond this
threshold were included in the phase 3 meta-analyses of non-European ancestry cohorts (see Supplementary
Fig. 1).
Gene-based association analyses
We conducted genome-wide gene-based association analysis using the second phase of the meta-analytic
results. We used the 19,427 protein-coding genes from the NCBI 37.3 gene definitions as the basis for the
gene-based association analysis using MAGMA
54
. We selected for each gene all SNPs within exonic, intronic
and untranslated regions of the gene as well as SNPs within 50 kb downstream and upstream of the gene.
After SNP annotation, there were 18,295 genes that were covered by at least one SNP. Gene-based
association tests were performed taking LD between SNPs into account. We applied a Bonferroni correction
to account for multiple testing, adjusting for the number of genes tested as well as the number of traits tested
(60 independent traits), setting the genome-wide threshold for significance at 4.5 x 10
−8
. These results are
shown in Supplementary Table 6.
Heritability due to common variants, genetic correlations and partitioned heritability
We used LD score regression
55,56
to estimate the proportion of variance accounted for by common SNPs or
SNP heritability (h
2
SNP
) of the global measures (total SA and average TH) and the SA and TH of each of the
34 cortical regions. These results are shown in Supplementary Table 7. LD score regression
56
was also used
to estimate genetic correlations between regions and with global measures. These results are shown in
Supplementary Table 12-13. We used a threshold of P ≤ 8.3 x 10
-4
(0.05/60) to correct for multiple testing in
the genetic and phenotypic correlations shown in Fig. 3. To identify patterns of genetic correlations of SA and
TH (both with and without correction for global measures), we used Mclust
57
for hierarchical cluster analysis,
which uses expectation-maximisation to fit parameterized Gaussian mixture models to the data. The best-
fitting model for number and shape of clusters was selected as the one with the largest Bayesian Information
Criterion. These results are shown in Supplementary Fig. 3.
Partitioned heritability analysis was used to estimate the percentage of heritability explained by annotated
regions of the genome
58
. Annotations were derived from either Epigenomics Roadmap
59
or a study of chromatin
accessibility in mid-fetal brains
19
. For analyses using Epigenomics Roadmap data, chromatin states (15 state
model) were downloaded for available tissue types
(http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html). For each tissue, genomic regions
comprising all active regulatory elements (TssA, TssAflnk, Enh, EnhG) within each tissue type were added as
an additional annotation to the baseline model provided with the LDSC package (https://github.com/bulik/ldsc).
For analyses using chromatin accessibility in mid-fetal brains, the genomic coordinates of peaks more
accessible in the germinal zone than the cortical plate (GZ>CP) and peaks more accessible in the cortical
plate than the germinal zone (CP>GZ) were added separately to the baseline annotations. Partitioned
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
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20
heritability and the enrichment of heritability explained in these annotations was run using LD-score
regression
58
. The significance of enrichment was corrected across all annotations used (including those not
displayed) using false discovery rate (FDR) and the enrichment scores were plotted as a heatmap for those
that survived significance (Fig. 2b).
Genetic correlations were calculated to determine if shared genetic influences contributed to both cortical
structure and neuropsychiatric disorders or psychological traits. Summary statistics were downloaded from the
following published genome-wide association studies: general cognitive function
28
, insomnia
46
, antisocial
behavior
60
, educational attainment
24
, subjective well-being
48
, depressive symptoms
48
, neuroticism
25
, attention
deficit hyperactivity disorder (ADHD)
47
, autism
61
, bipolar disorder
62
, anorexia nervosa
63
, major depressive
disorder
49
, obsessive compulsive disorder
64
, post-traumatic stress disorder (PTSD)
65
, schizophrenia
66
, anxiety
disorders
67
, aggression
68
, Alzheimer's disease
69
, loneliness
70
, cigarettes smoked per day
71
, epilepsy
72
,
Parkinson's disease
23
, frontotemporal dementia
73
, and irritable bowel disease
74
. LD-score regression was used
to calculate genetic correlations
56
. Significance was corrected for multiple comparisons using FDR across all
genetic correlations with average TH and total SA, and significant associations were highlighted in Fig. 5. To
explore regional variability in those significant genetic correlations, genetic correlations were conducted
between the trait and the cortical regions (without correcting for global measures). These results are shown in
Supplementary Tables 8 and 14.
Multivariate GWAS analysis
We used TATES
33
to conduct two multivariate analyses: one for the 34 regional SA measures, and one for the
34 regional TH measures. These analyses were run on the meta-analytic results from the second phase of
meta-analysis. Briefly, TATES combines the p-values from univariate GWAS while correcting for the
phenotypic correlations between traits and does not require access to raw genotypic data
33
. The power of
TATES has been shown to be similar or greater than that of multivariate tests using raw data across a range
of scenarios for analyses of 20 or more traits
75
. For these analyses, we used phenotypic correlations calculated
from the UK Biobank cohort (residuals correcting for sex, age, ancestry, and global brain measures).
Gene-set enrichment analyses
Gene-set enrichment analyses were performed on total SA and average TH as well as the multivariate GWAS
results for SA and TH using DEPICT
76
. Within DEPICT, groups of SNPs were assessed for enrichment in
14,462 gene-sets. These analyses were run using variants with P ≤ 1.0 x 10
-5
. Gene-set enrichment analyses
were considered significant if they survived FDR correction (q ≤ 0.05)
76
. These results are shown in
Supplementary Table 9.
Functional annotation
Potential functional impact was investigated for each of the 251 SNPs nominally associated with global and
regional SA and TH and for their proxies (defined here as r
2
> 0.6 to the lead SNP) using a number of publicly
available data sources. The majority of the SNP annotations were as provided by FUMA
26
which annotates
SNP location (e.g., genic/intergenic), the potential for functional effects through predicted effects as determined
by CADD
77
and Regulome
78
, expression quantitative trait (eQTL) effects (e.g., GTEx, the UK Brain Expression
Consortium (http://www.braineac.org/), and the CommonMind Consortium
81
), and chromatin state and
interactions in numerous tissues (data from 21 tissue/cell types, GEO GSE87112). These data were used by
FUMA to map coding and non-coding (e.g., lincRNA) genes to each lead SNP based on an eQTL effect with
an FDR correction P ≤ 0.05 in cortical tissue, and/or chromatin interactions between the region harbouring the
lead SNP and a gene promoter in a second chromosomal region (including interactions with an FDR correction
P ≤ 1 x 10
-6
) in dorsolateral prefrontal cortex and neural progenitor cells
26
. HaploReg
79
was used to annotate
transcription factor binding across multiple tissues, and whether SNPs modified transcription factor binding
motifs. The potential for a detrimental effect on protein function due to lead or proxy SNPs located within gene
exons was investigated using SIFT and PolyPhen as reported by SNPNexus
80
. Pre- and post-natal gene
expression data across multiple brain regions was obtained from the BrainSpan Atlas of the Developing Human
Brain (http://www.brainspan.org/). Summaries of the functional annotation data are presented in
Supplementary Tables 10–11.
Analysis of the central sulcus
To follow-up the precentral surface area association with rs1080066, 10,557 UK Biobank MRI scans were
further analyzed using BrainVISA-4.5 Morphologist pipeline for the extraction and parameterization of the
central sulcus. Quality controlled FreeSurfer outputs (orig.mgz, ribbon.mgz and talairach.auto) were directly
imported into the pipeline to use the same gray and white matter segmentations. Sulci were automatically
labeled according to a predefined anatomical nomenclature of 60 sulcal labels per hemisphere
81,82
. Extracted
meshes for the left and right central sulcus were visually quality checked; subjects with mislabelled central
sulcus were discarded from further analysis; 6,045 individuals had good quality extractions for both the left and
right hemispheres. The central sulcus depth profile was measured by extending the method introduced in
37,83
.
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21
The ridges at the fundus of the sulcus and at the convex hull, along with the two extremities, were automatically
extracted. Using these landmarks, two coordinate fields (x and y) were extrapolated over the entire mesh
surface
84
. Sulcal depth was defined as the distance between paired points at the sulcal fundus and brain
envelope that shared the same y coordinate
85
. For each individual, the parametrized surface was divided into
100 equally spaced points along the length of the sulcus, and the depth at each point was recorded for
comparison. We averaged the corresponding depth measurements across the left and right sulcus and
calculated the effect of the rs1080066 G allele on the bilaterally averaged depth at each point. These results
are shown in Fig. 4d.
Estimating linkage disequilibrium (LD) with the 5-HTTLPR variable number tandem repeat.
Using PLINK
86
, we estimated the LD between rs6505147 and the 5-HTTLPR variable number tandem repeat
using data from 807 unrelated founders from the QTIM sample who are genotyped for 5-HTTLPR and have
rs6505147 imputed (imputation accuracy r
2
= 0.965). These analyses showed the two genotypes to be
unlinked, R
2
= 0.066, D' = 0.267.
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23
Extended Data Figures
Supplementary Figure 1. Flow chart summarising the phases of meta-analysis.
Supplementary Figure 2. Results of co-localisation analyses for a) the 17q21.31 inversion region and b) the
6q21 region.
Supplementary Figure 3. Clustering of genetic correlations among a) surface area and b) thickness regions
after correcting for global measures. Clustering of genetic correlations among c) surface area and d) thickness
regions without correcting for global measures. The best-fitting model for surface area with global correction
was 3 diagonal components with varying volume and shape, and for thickness was 6 spherical components
with equal volume. The best-fitting model for surface area without global correction was 5 spherical
components with varying volume, and for thickness was 8 spherical components with equal volume.
Supplementary Figure 4. P-value of genome-wide significant regional SNPs with global control compared to
their P-value in the global measure for a) surface area and b) thickness. Effect size of genome-wide significant
regional SNPs with global control compared to their effect size in global measures for c) surface area and d)
thickness. Effect size of genome-wide significant regional SNPs with global control compared to regional SNPs
without global control in e) surface area and f) thickness.
Supplementary Figure 5. Regional association plot for the 3p24.1 locus (rs12630663)
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24
Supplementary Notes
Functional annotation
Chromatin state represents the degree to which 200 bp genomic regions are accessible for transcription.
Around each of our associated loci, chromatin state was annotated by FUMA
26
utilising the core 15-state model
as predicted by Roadmap Epignomics using ChromHMM, based on data for 5 chromatin state marks
(H3K4me3, H3K4me1, H3K36me3, H3K27me3, H3K9me3) in 127 epigenomes
20
. In Fig. 5, genomic regions
in three tissues/cells most relevant to our study (RoadMap E073 dorsolateral prefrontal cortex [Adult cortex],
E081 female fetal brain [Fetal brain], and E125 NH-A Astrocytes Primary Cells [Astrocytes]) are indicated as
one of the 15 possible chromatin states, as outlined in Table 1.
Table 1. RoadMap Core 15-chromatin state model abbreviations and descriptors
STATE # Abbreviation Description
1 TssA Active Transcription Start Site (TSS)
2 TssAFlnk Flanking Active TSS
3
TxFlnk Transcription at gene 5' and 3'
4
Tx Strong transcription
5
TxWk Weak transcription
6
EnhG Genic enhancers
7
Enh Enhancers
8
ZNF/Rpts ZNF genes & repeats
9
Het Heterochromatin
10
TssBiv Bivalent/Poised TSS
11
BivFlnk Flanking Bivalent TSS/Enhancer
12
EnhBiv Bivalent Enhancer
13
ReprPC Repressed PolyComb
14
ReprPCWk Weak Repressed PolyComb
15 Quies Quiescent/Low
To aid in the identification of candidate genes influencing cortical development we examined gene expression
levels in pre- and post-natal brains for each of the genes of interest (primarily cortical eQTLs and/or genes
with a known brain association/function) flanking our associated loci. Here we used data included in the
Developmental Transcriptome portal of the BRAINSPAN Atlas of the Developing Human Brain website
(http://www.brainspan.org/rnaseq/searches?search_type=user_selections). These data include gene
expression information for cortical tissues indicated on a scale from low (dark blue) to high (dark red)
expression on a log
2
RPKM scale (RPKM = Reads Per Kilobase [of transcript per] Million [mapped reads],
which normalises expression levels to account for sequencing depth and gene length). The BRAINSPAN
cortical tissues, organised in ontological order, are as outlined in Table 2.
Table 2. Brainspan cortical tissues abbreviations and descriptors
Abbreviation Description
DFC dorsolateral prefrontal cortex
VFC ventrolateral prefrontal cortex
MFC anterior
(
rostral
)
cin
g
ulate
(
medial prefrontal
)
corte
x
OFC orbital frontal corte
x
M1C primar
y
motor cortex
(
area M1, area 4
)
M1C-S1C primar
y
motor-sensor
y
cortex
(
samples
)
PCx parietal neocortex
S1C primar
y
somatosensor
y
cortex
(
area S1, areas 3,1,2
)
IPC posteroventral
(
inferior
)
parietal corte
x
A
1C primar
y
auditor
y
cortex
(
core
)
TC
x
temporal neocorte
x
STC posterior
(
caudal
)
superior temporal cortex
(
area 22c
)
ITC inferolateral temporal cortex
(
area TEv, area 20
)
Oc
x
occipital neocortex
V1C primar
y
visual cortex
(
striate cortex, area V1/17
)
For each locus, we evaluated functional annotations for the lead SNP and for additional SNPs considered to
be credible causal variants (CCVs) if they were either i) in reasonable LD (R
2
≥ 0.6 in individuals of European
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25
ancestry) with the lead SNP and/or ii) had P-values within 2 orders of magnitude of the lead SNP. As lincRNAs
show considerable cell/tissue specificity, in the main text we detail SNP location based on neighbouring coding
genes, but detail lincRNAs when our lead SNPs show eQTL effects and/or chromatin interactions to these
non-coding transcripts.
Genes at each associated locus were determined to be potential candidates by considering whether the lead
SNP (or a proxy) was an eQTL for a particular gene in adult cortical tissue (e.g. BRAINEAC, CMC or GTEx
cortical tissues) and/or when chromatin interactions were observed to occur between the region harbouring
the lead/proxy SNPs and a gene promoter in relevant brain tissues (dorsolateral prefrontal cortex and/or neural
progenitor cells). For each of the loci shown in the main text we determined potential candidates as (but not
limited to) the following:
Loci associated with Total Surface Area:
rs62057153, chromosome 17q21.31
Located in intron 4 of the Corticotropin Releasing Hormone Receptor 1(CRHR1) gene, lead SNP rs62057153
is in a large inversion region with extensive LD across ~0.45 Mb. Due to the LD structure there are > 4000
CCVs at this locus, making identification of a causal variant and its gene/s targets difficult. Of note, these CCVs
are eQTLs (Supplementary Table 11) for 21 coding and non-coding genes in cortical tissue, including CRHR1
87
(FDR P
CMC
= 0.009) and other genes known to be involved in brain development such as Wnt Family Member
3 (WNT3: FDR P
CMC
= 0.009), Microtuble Associated Protein Tau (MAPT
88
: FDR P
CMC
= 0.009) and KAT8
Regulatory NSL Complex Subunit 1 (KANSL1
89
: FDR P
CMC
= 0.009) (Supplementary Table 11). Of the CCVs
59 are exonic variants: 5 of these are non-synonymous variants predicted to alter the function of MAPT
(rs12185233, rs17651549, rs63750417), KANSL1 (rs35833914) and SPPL2C (rs12373123).
rs1628768, chromosome 10q24.33
Lead SNP rs1628768 is located between the coding genes 5'-Nucleotidase, Cytosolic II (NTC52) and
Internexin Neuronal Intermediate Filament Protein Alpha (INA). In addition to rs1628768 there are 22 additional
CCVs at this locus, one of which (rs7911488) is located within ATP Synthase Membrane Subunit DAPIT
ATP5MD/USMG5 intron 1, in a region of histone binding indicating promoter and enhancer activity in
dorsolateral prefrontal cortex and fetal brain (RoadMap Epigenome tissues E073, E082 and E081; Fig. 2d).
There is evidence for an eQTL effect for USMG5 and a number of additional genes in various non-brain tissues
(Supplementary Table 11), while in cortical tissue these CCVs are eQTLs for INA, encoding the fourth subunit
of neuronal filaments in the adult central nervous system
89
, C10orf32, WW Domain Binding Protein 1 Like
(WBP1L), and the schizophrenia candidate genes Arsenite Methyltransferase (AS3MT) and NT5C2
89
.
rs2802295, chromosome 6q21
Lead SNP rs2802295 is located within intron 1 of the Forkhead Box O3 (FOXO3) gene. This SNP is a cortical
eQTL for FOXO3 and for Zinc Finger Protein 259 Pseudogene 1 (ZNF259P1; Supplementary Table 11). In
mouse models Foxo3 has been shown to be linked to numerous, including neuronal death and neurotoxic
amyloid beta peptide processing in an Alzheimer’s model
90
).
rs34464850, chromosome 3q23
Lead SNP rs34464850 is located within intron 4 of the Transcription Factor Dp-2 (TFDP2) gene. This SNP is
an eQTL for ATPase Na+/K+ Transporting Subunit Beta 3 (ATP1B3), G Protein-Coupled Receptor Kinase 7
(GRK7) and TFDP2 in whole blood (Supplementary Table 11).
rs190958130, chromosome 6q22.32
Eleven significant associations with various phenotypes were identified across the 6q22.32 region
(Supplementary Table 10), comprising three independent genomic loci (R
2
<0.4, Supplementary Table 10).
Lead SNP rs190958130 is a low frequency variant (minor allele frequency (MAF) ~ 0.4) located between the
Centromere Protein W (CENPW) and R-Spondin 3 (RSPO3) genes. This SNP is in very close proximity but
low LD to a higher frequency SNP, rs4273712 (MAF ~ 0.37, R
2
rs190958130-rs4273712
= 0.011), associated with total,
caudal middle frontal and parahippocampal SA in this study, and also previously associated with intracranial
volume. CENPW is an interesting candidate as while a direct link between this gene and cortical development
has not been reported, many other centrosomal proteins have been implicated in microcephaly
91
, with
alterations in centrosomal proteins impacting cell proliferation and hence cortex structure.
rs190958130 and rs4273712, chromosome 6q22.32
Lead SNP rs11171739 is located between the Ribosomal Protein S26 (RPS26) and Erb-B2 Receptor Tyrosine
Kinase 3 (ERBB3) genes. This low frequency SNP (MAF = 0.04) is a cortical eQTL for RPS26 (P
GTEX Brain cortex
= 7.4 x 10
-44
), ERBB3 (FDR P
CMC
= 0.009) and for Sulfite Oxidase (SUOX; P
GTEX Brain cortex
= 5.5 x 10
-8
). In mice,
Erbb3 has been shown to bind Erbb4, essential for neuronal survival
92
. SUOX mutations are responsible for
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26
Isolated Sulphite Oxidase Deficiency, a neurometabolic disease with multiple neurological findings including
substantial neuronal loss
93
.
Lead SNP rs4273712 is also located between CENPW and RSPO3 genes, although there is no LD between
this common SNP and the lower frequency variant rs190958130. Proxy SNPs for rs4273712, along with other
SNPs in the region associated with various other phenotypes (rs2184968 and rs9401907, pairwise R
2
between
these SNPs ≤ 0.25; Supplementary Table 10), are cortical eQTLs for the TRNA Methyltransferase 11 Homolog
(TRMT11: FDR P
CMC
= 0.049) gene. This SNP was previously associated with intracranial volume
15
.
Loci associated with Average Thickness:
rs630934, chromosome 3p22.1
Lead SNP rs630934 is located between the Ribosomal Protein SA (RPSA) and Myelin-Associated
Oligodendroctye Basic Protein (MOBP) genes. MOBP is an interesting candidate gene for cortical thickness
due to its role in myelination, and that expression is higher post- than pre-natally (Fig. 2f). However, an eQTL
association with MOBP is seen only in GTEx tibial nerve tissue, with more extensive eQTL associations seen
for other nearby genes, particularly RPSA, in other tissue types (Supplementary Table 11). RPSA, with
functions as a ribosomal protein and a non-integrin laminin receptor, has been shown to have a role in neuronal
migration through a functional interaction with ZNF804A
94
. There are no chromatin interactions or cortical
eQTLs associated with rs630934 or its proxies in cortical data currently available in the FUMA database.
rs11692435, chromosome 2q11.2
Lead SNP rs11692435 is located within exon ARP1 Actin Related Protein 1 Homolog B (ACTR1B) gene. While
there are other CCVs at this locus, rs11692435 is itself an interesting candidate causal SNP as it causes an
amino acid replacement predicted to be ‘damaging’ to protein function by SIFT and ‘probably damaging’ by
PolyPhen (http://www.snp-nexus.org/; Supplementary Table 10). This SNP and its proxies are eQTLs for
ACTR1B and for other nearby genes in numerous tissues (Supplementary Table 11).
Regional Associations:
rs1080066, chromosome 15q14 (precentral SA, rostral middle frontal SA)
Located within intron 1 of lincRNA RP11-624L4.1 and flanked by coding genes RA Guanyl Releasting Protein
1 (RASGRP1) and Thrombospondin 1 (THBS1), lead SNP rs1080066 was the most significant association
(with precentral SA) detected across all of our phenotypes. Another four SNPs in very high LD with rs1080066
were associated with six additional SA and TH phenotypes. A sixth SNP, rs4923822 located ~22 Kb
downstream and associated with transverse temporal SA, is in moderate LD (R
2
~0.39 in Europeans) to the
other five SNPs comprising this independent genomic locus (Supplementary Table 10).
Accross the six lead SNPs overlapping CCV sets include up to 14 SNPs, although this can be reduced to a
minimum set of 10 that includes all six lead SNPs. This minimal set is indicated as influencing the expression
of THBS1, and a non-coding RNA co-located with THBS1 exons 18 and 19 (CTD-2033D15.1), in whole blood
(Supplementary Table 11). These CCVs are located in a chromosomal region that interacts with downstream
regions housing the promoters of RP11-624L4.1 and THBS1 in neural progenitor cells (Fig 4a; Supplementary
Table 11). THBS1 is also implicated as a candidate gene for two (lead SNPs rs3862145 [postcentral SA] and
rs78502100 [supramarginal SA]) of the additional five independent genomic loci in this cytoband, through
chromatin interactions between chromosomal regions housing these SNPs and the THBS1 promoter in neural
progenitor cells (Supplementary Table 11). THBS1 has a role in synaptogenesis and the maintenance of
synaptic integrity
95
.
rs73313052, chromosome 14q23.1 (precuneus, pericalcarine and cuneus SA)
Lead SNP rs73313052 is one of five SNPs associated with six phenotypes at this independent genomic locus
(Supplementary Table 10). These SNPs are located between the coding genes Dishevelled Binding Antagonist
of Beta Catenin 1 (DACT1), with a role in synapse development
39
, and Dishevelled Associated Activator of
Morphogenesis 1 (DAAM1), recently shown to have a role in neuronal dendritic protrusion morphology
96
. There
are two additional independent genomic loci located in this cytoband (Supplementary Table 10), one
centromeric to rs73313052 and upstream of DACT1 (most significant SNP rs160459, middle temporal TH) and
one telomeric and at the 3’ end of DAAM1 (most significant SNP rs17834032, fusiform SA). Within each locus
LD between SNPs is high (R
2
> 0.8), but there is no LD (r2 = 0) between SNPs comprising the different loci.
Across all three genomic loci there is evidence for an influence on DAAM1. The locus represented by
rs73313052 is located in the DAAM1 promoter region (upstream and including DAAM1 intron 1; Fig 4e), with
SNPs here acting as cortical eQTLs for DAAM1 (e.g. rs73313052 FDR P
CMC
= 0.049) and the nearby genes
G Protein-Coupled Receptor 135 (GPR135) and Trans-L-3-Hydroxyproline Dehydratase (L3HYPDH). Both of
the other loci in this cytoband show chromatin interactions from the regions of the lead SNPs to that of the
DAAM1 promoter in neural progenitor cells, with SNPs from the locus represented by rs17834032 also acting
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27
as cortical eQTLs for DAAM1, GPR135 and L3HYPDH. High LD proxies are located within an active
transcription start site in adult cortex and fetal brain tissue (Fig. 4e).
rs910697 and rs2999158, chromosome 1p13.2
Lead SNPs rs910697 (lingual SA) and rs2999158 (pericalcarine SA) are in moderate LD with each other (R
2
= 0. 57), and comprise independent genomic locus 9 (Supplementary Table 10). These SNPs and their proxies
are cortical eQTLs for Wnt pathway genes Suppression Of Tumorigenicity 7 Like (ST7L) and Wnt Family
Member 2B (WNT2BI: FDR P
CMC
= 0.009 for both SNPs for both genes), in addition to Capping Actin Protein
Of Muscle Z-Line Subunit Alpha 1 (CAPZA1: P
GTEx Brain cortex
= 1.7 x 10
-5
) and the non-coding RNA RP4-
671G15.2 (P
GTEx Brain cortex
= 7.1 x 10
-9
).
rs11161942 and rs1413536, chromosome 1p22.2
These two lead SNPs represent independent loci (R
2
= 0) associated with the SA of the posterior cingulate
(rs11161942) and inferior parietal (rs1413536). A third independent locus within this chromosomal region
(rs59373415) is associated with the SA of the precuneus (Supplementary Table 5). All are of interest due to
their proximity to and predicted functional influence on the LIM Domain Only 4 (LMO4) gene (Supplementary
Table 11). rs1413536 and 15 additional CCVs are cortical eQTLs for LMO4 (FDR P
CMC
= 0.009), with chromatin
interactions between the region housing these SNPs and the LMO4 promoter (Supplementary Table 11).
rs6505147, chromosome 17q11.2
Lead SNP rs6505147 is associated with the SA of the insula. Located within a large block of LD spanning ~680
Kb, this SNP is of interest due to its proximity to the serotonin transporter SLC6A4, located at the telomeric
end of the LD block. Due to the extensive LD there are an additional 276 CCVs at this locus, hence determining
a causal SNP at this locus will be challenging. rs6505147 and its proxies are eQTLs for numerous genes
across the region in cortical tissue (Coronin (CORO6), Slingshot Protein Phosphatase 2 (SSH2), EF-Hand
Calcium Binding Domain 5 (EFCAB5), Bleomycin Hydrolase (BLMH), Golgi SNAP Receptor Complex Member 1
(GOSR1), SUZ12P1 PRS4 Recombination Region (SUZ12P)), with evidence for promoter activity particularly in
the region of the SSH2, EFCAB5 and BLMH promoters (Fig 4h). Evidence for a role for SLC6A4 in insula SA
is less convincing, with little evidence of promoter activity or transcription in adult cortex, fetal brain or
astrocytes (Fig 4h). There are, however, chromatin interactions to the region of the SLC6A4 promoter observed
in neural progenitor cells, and rs6505147 and its proxies are eQTLs for SLC6A4 in other tissue types (whole
blood, esophagus, tibial nerve and testis, Supplementary Table 11). SLC6A4 is an obvious candidate gene at
this locus that has been extensively studied in relation to behavioural traits. Concomitant with a proposed role
in mental health issues associated with stress, 5-HTT knockout mice show behavioural and cortical
morphological abnormalities that alter their responses to trauma and stress
97
. However, a recent meta-analysis
of 31 human studies, including 38 802 individuals of European ancestry, found no clear association between
depression, stress and 5-HTTLPR
42
. As for all other loci, determining the candidate SNP and the gene it
influences will require further bioinformatic and functional laboratory work.
Sulcal development
Positive genetic correlations between the SA of neighbouring regions may also be driven by the development
of the sulcus, separating the regions. The pre- and post- central regions (also known as the primary motor and
sensorimotor cortices, respectively) are consistently labelled across many cortical atlases as the regions
directly anterior and posterior to the central sulcus (which appears early in development
98
). The SA of all four
regions surrounding the calcarine sulcus (the pericalcarine, lingual, cuneus, and lateral occipital region) show
positive genetic correlations. The same is also true for the SA of the insula and superior temporal gyri
surrounding the lateral sulcus (or Sylvian fissure). These major, early forming sulci, show positive genetic
correlations for SA, but not TH, of regions that directly surround them. These observations may imply that part
of the genetic influences we observe to be underlying regional SA, may actually be driving the formation of the
separating folds, or sulci, during fetal development.
The Desikan-Killiany atlas
The Desikan-Killiany atlas
12
used here to define the 34 regions of interest is one of many possible atlases. It
is one of the coarser atlases, yielding larger, more consistent regions, defined by the common folding patterns
visible on standard MRI. More recent efforts partitioning the cortex into 180 regions have used high-resolution
multimodal assessments (MMPC)
99
. It is possible that positive correlations between adjacent structures may
reflect suboptimal partitioning of the cortex by the Desikan-Killiany atlas into distinct functional brain regions;
for example, we see a positive genetic correlation between the inferior parietal and the superior parietal gyri,
whereas in the MMPC atlas, a portion of each of these two regions is included under the intraparietal labels.
Portions of these genetically correlated regions may in future be re-assigned based on other advanced imaging
data, such as multimodal myelin mapping, which may better define cortical cellular architecture.
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28
Consortium Authors (
#
)
Alzheimer’s Disease Neuroimaging Initiative (ADNI). Data used in preparing this article were obtained from
the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, many
investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but
did not participate in analysis or writing of this report. A complete listing of ADNI investigators may be found
at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
ADNI Infrastructure Investigators: Michael Weiner (UC San Francisco), Paul Aisen (University of Southern
California), Ronald Petersen (Mayo Clinic, Rochester), Clifford R. Jack, Jr. (Mayo Clinic, Rochester), William
Jagust (UC Berkeley), John Q. Trojanowki (U Pennsylvania), Arthur W. Toga (USC), Laurel Beckett (UC),
Davis Robert C. Green (Brigham and Women’s Hospital/Harvard Medical School), Andrew J. Saykin (Indiana
University), John Morris (Washington University St. Louis), Leslie M. Shaw (University of Pennsylvania). ADNI
External Advisory Board (ESAB): Zaven Khachaturian (Prevent Alzheimer’s Disease 2020 (Chair)), Greg
Sorensen (Siemens), Maria Carrillo (Alzheimer’s Association), Lew Kuller (University of Pittsburgh), Marc
Raichle (Washington University St. Louis), Steven Paul (Cornell University), Peter Davies (Albert Einstein
College of Medicine of Yeshiva University), Howard Fillit (AD Drug Discovery Foundation), Franz Hefti
(Acumen Pharmaceuticals), David Holtzman (Washington University St. Louis), M. Marcel Mesulam
(Northwestern University), William Potter (National Institute of Mental Health), Peter Snyder (Brown
University). ADNI 3 Private Partner Scientific Board (PPSB): Veronika Logovinsky,(Eli Lilly (Chair)). Data and
Publications Committee: Robert C. Green (BWH/HMS (Chair)). Resource Allocation Review Committee: Tom
Montine (University of Washington (Chair)). Clinical Core Leaders: Ronald Petersen (Mayo Clinic, Rochester
(Core PI)), Paul Aisen (University of Southern California). Clinical Informatics and Operations: Gustavo
Jimenez (USC), Michael Donohue (USC), Devon Gessert (USC), Kelly Harless (USC), Jennifer Salazar (USC),
Yuliana Cabrera (USC), Sarah Walter (USC), Lindsey Hergesheimer (USC). Biostatistics Core Leaders and
Key Personnel: Laurel Beckett (UC Davis (Core PI)), Danielle Harvey (UC Davis), Michael Donohue (UC San
Diego). MRI Core Leaders and Key Personnel: Clifford R. Jack, Jr. (Mayo Clinic, Rochester (Core PI)), Matthew
Bernstein (Mayo Clinic, Rochester), Nick Fox (University of London), Paul Thompson (UCLA School of
Medicine), Norbert Schuff (UCSF MRI), Charles DeCArli (UC Davis), Bret Borowski (RT Mayo Clinic), Jeff
Gunter (Mayo Clinic), Matt Senjem (Mayo Clinic), Prashanthi Vemuri (Mayo Clinic), David Jones (Mayo Clinic),
Kejal Kantarci (Mayo Clinic), Chad Ward (Mayo Clinic). PET Core Leaders and Key Personnel: William Jagust
(UC Berkeley (Core PI)), Robert A. Koeppe (University of Michigan), Norm Foster (University of Utah), Eric M.
Reiman (Banner Alzheimer’s Institute), Kewei Chen (Banner Alzheimer’s Institute), Chet Mathis (University of
Pittsburgh), Susan Landau (UC Berkeley). Neuropathology Core Leaders: John C. Morris (Washington
University St. Louis), Nigel J. Cairns (Washington University St. Louis), Erin Franklin (Washington University
St. Louis), Lisa Taylor-Reinwald (Washington University St. Louis – Past Investigator). Biomarkers Core
Leaders and Key Personnel: Leslie M. Shaw (UPenn School of Medicine), John Q. Trojanowki
(UPenn School
of
Medicine), Virginia Lee (UPenn School of Medicine), Magdalena Korecka (UPenn School of Medicine),
Michal Figurski (UPenn School of Medicine). Informatics Core Leaders and Key Personnel: Arthur W. Toga
(USC (Core PI)), Karen Crawford (USC), Scott Neu (USC). Genetics Core Leaders and Key Personnel:
Andrew J. Saykin (Indiana University), Tatiana M. Foroud (Indiana University), Steven Potkin (UC Irvine), Li
Shen (Indiana University), Kelley Faber (Indiana University), Sungeun Kim (Indiana University), Kwangsik Nho
(Indiana University). Initial Concept Planning & Development: Michael W. Weiner (UC San Francisco), Lean
Thal (UC San Diego), Zaven Khachaturian (Prevent Alzheimer’s Disease 2020). Early Project Proposal
Development: Leon Thal (UC San Diego), Neil Buckholtz (National Institute on Aging), Michael W. Weiner (UC
San Francisco), Peter J. Snyder (Brown University), William Potter (National Institute of Mental Health), Steven
Paul (Cornell University), Marilyn Albert (Johns Hopkins University), Richard Frank (Richard Frank Consulting),
Zaven Khachaturian (Prevent Alzheimer’s Disease 2020). NIA: John Hsiao (National Institute on Aging). ADNI
Investigators by Site: Oregon Health & Science University: Joseph Quinn, Lisa C. Silbert, Betty Lind, Jeffrey
A. Kaye – Past Investigator, Raina Carter – Past Investigator, Sara Dolen – Past Investigator. University of
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– Past Investigator. University of California – San Diego: James Brewer, Helen Vanderswag, Adam Fleisher –
Past Investigator. University of Michigan: Jaimie Ziolkowski, Judith L. Heidebrink, Joanne L. Lord – Past
Investigator. Mayo Clinic, Rochester: Ronald Petersen, Sara S. Mason, Colleen S. Albers, David Knopman,
Kris Johnson – Past Investigator. Baylor College of Medicine: Javier Villanueva-Meyer, Valory Pavlik,
Nathaniel Pacini, Ashley Lamb, Joseph S. Kass, Rachelle S. Doody – Past Investigator, Victoria Shibley –
Past Investigator, Munir Chowdhury – Past Investigator, Susan Rountree – Past Investigator, Mimi Dang –
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Randy Yeh. Washington University, St. Louis: Beau Ances, John C. Morris, David Winkfield, Maria Carroll,
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Investigator. University of Alabama - Birmingham: Daniel Marson, David Geldmacher, Marissa Natelson Love,
Randall Griffith – Past Investigator, David Clark – Past Investigator, John Brockington – Past Investigator.
Mount Sinai School of Medicine: Hillel Grossman, Effie Mitsis – Past Investigator. Rush University Medical
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Sosa, Megan Witbracht. University of Texas Southwestern Medical School: Kyle Womack, Dana Mathews,
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Center: Jeffrey M. Burns, Russell H. Swerdlow, William M. Brooks. University of California, Los Angeles: Ellen
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Tingus – Past Investigator, Po H. Lu – Past Investigator, George Bartzokis – Past Investigator. Mayo Clinic,
Jacksonville: Neill R Graff-Radford (London), Francine Parfitt, Kim Poki-Walker. Indiana University: Martin R.
Farlow, Ann Marie Hake, Brandy R. Matthews – Past Investigator, Jared R. Brosch, Scott Herring. Yale
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Montreal-Jewish General Hospital: Howard Chertkow, Howard Bergman, Chris Hosein. Sunnybrook Health
Sciences, Ontario: Sandra Black, Bojana Stefanovic, Chris (Chinthaka) Heyn. U.B.C. Clinic for AD & Related
Disorders: Ging-Yuek Robin Hsiung, Benita Mudge, Vesna Sossi, Howard Feldman – Past Investigator,
Michele Assaly – Past Investigator. Cognitive Neurology - St. Joseph's, Ontario: Elizabeth Finger, Stephen
Pasternack, William Pavlosky, Irina Rachinsky – Past Investigator, Dick Drost – Past Investigator, Andrew
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Bonakdarpour, Diana Kerwin – Past Investigator, Chuang-Kuo Wu,– Past Investigator, Nancy Johnson – Past
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L. Taylor, Steven Chao, Barton Lane – Past Investigator, Allyso
n Rosen – Past Investigator, Jared Tinklenberg
– Past Investigator. Banner Sun Health Research Institute: Edward Zamrini, Christine M. Belden, Sherye A.
Sirrel. Boston University: Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash – Past
Investigator, Patricia Lynn Johnson – Past Investigator. Howard University: Thomas O. Obisesan, Ntekim E.
Oyonumo, Joanne Allard, Olu Ogunlana. Case Western Reserve University: Alan Lerner, Paula Ogrocki, Curtis
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D. Pearlson, Karen Blank, Karen Anderson. Dartmouth-Hitchcock Medical Center: Laura A. Flashman, Marc
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Kaycee M. Sink, Mia Yang, Akiva Mintz. Rhode Island Hospital: Brian R. Ott, Geoffrey Tremont, Lori A. Daiello.
Butler Hospital: Courtney Bodge, Stephen Salloway, Paul Malloy, Stephen Correia, Athena Lee. UC San
Francisco: Howard J. Rosen, Bruce L. Miller, David Perry. Medical University South Carolina: Jacobo Mintzer,
Kenneth Spicer, David Bachman. St. Joseph’s Health Care: Elizabeth Finger, Stephen Pasternak, Irina
Rachinsky, John Rogers, Andrew Kertesz – Past Investigator, Dick Drost – Past Investigator. Nathan Kline
Institute: Nunzio Pomara, Raymundo Hernando, Antero Sarrael. University of Iowa College of Medicine:
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CHARGE Consortium: Edith Hofer (Clinical Division of Neurogeriatrics, Department of Neurology, Medical
University of Graz, Graz, Austria), Gennady V. Roshchupkin (Department of Radiology and Nuclear Medicine,
Erasmus MC, Rotterdam, The Netherlands), Hieab H. H. Adams (Department of Radiology and Nuclear
Medicine, Erasmus MC, Rotterdam, The Netherlands), Maria J. Kno (Department of Epidemiology, Erasmus
MC, Rotterdam, The Netherlands), Honghuang Lin (Section of Computational Biomedicine, Department of
Medicine, Boston University School of Medicine, Boston, MA, USA), Shuo Li (Department of Biostatistics,
Boston University School of Public Health, Boston, MA, USA), Habil Zare (Department of Computer Science,
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Texas State University, San Marcos, Texas, USA.), Shahzad Ahmad (Department of Epidemiology, Erasmus
MC, Rotterdam, The Netherlands), Nicola J. Armstrong (Mathematics and Statistics, Murdoch University,
Perth, Australia), Manon Bernard (Hospital for Sick Children, Toronto, Canada), Josh Bis (Cardiovascular
Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA), Nathan A.
Gillespie (Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, VA,
USA), Michelle Luciano (Centre for Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh,
Edinburgh, UK), Aniket Mishra (University of Bordeaux, Bordeaux Population Health Research Center,
INSERM, Bordeaux, France), Markus Scholz (Institute for Medical Informatics, Statistics and Epidemiology,
University of Leipzig, Leipzig, Germany), Alexander Teumer (Institute for Community Medicine, University
Medicine Greifswald, Greifswald, Germany), Rui Xia (Institute of Molecular Medicine and Human Genetics
Center, University of Texas Health Science Center at Houston, Houston, TX, USA), Xueqiu Jian (Institute of
Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston,
Houston, TX, USA), Thomas H. Mosley (Department of Medicine, University of Mississippi Medical Center,
Jackson, MS, USA), Yasaman Saba (Gottfried Schatz Research Center for Cell Signaling, Metabolism and
Aging, Medical University of Graz, Graz, Austria), Lukas Pirpamer (Clinical Division of Neurogeriatrics,
Department of Neurology, Medical University of Graz, Graz, Austria), Stephan Seiler (Clinical Division of
Neurogeriatrics, Department of Neurology, Medical University of Graz, Graz, Austria), Uwe Völker (Interfaculty
Institute for Genetics and Functional Genomics, University Medicine Greifswald), James T. Becker
(Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh, PA, USA), Owen
Carmichael (Pennington Biomedical Research Center, Baton Rouge, LA, USA), Jerome I. Rotter (Institute for
Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Pediatrics
at Harbor-UCLA Medical Center, Torrance, CA, USA), Bruce M. Psaty (Cardiovascular Health Research Unit,
Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA),
Oscar L. Lopez (Departments of Psychiatry, Neurology, and Psychology, University of Pittsburgh, Pittsburgh,
PA, USA), Najaf Amin (Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands), Sven J. van
der Lee (Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands), Qiong Yang (Department
of Biostatistics, Boston University School of Public Health, Boston, MA, USA), Claudia L. Satizabal
(Department of Epidemiology and Biostatistics, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative
Diseases, UT Health San Antonio, San Antonio, USA), Jayandra J. Himali (Department of Biostatistics, Boston
University School of Public Health, Boston, MA, USA), Pauline Maillard (Imaging of Dementia and Aging (IDeA)
Laboratory, Department of Neurology, University of California-Davis, Davis, CA, USA), Alexa S. Beiser
(Department of Neurology, Boston University School of Medicine, Boston, MA, USA), Charles DeCarli
(Department of Neurology and Center for Neuroscience, University of California at Davis, Sacramento, CA,
USA), Sherif Karama (McGill University, Montreal Neurological Institute, Montreal, Canada), Lindsay Lewis
(McGill University, Montreal Neurological Institute, Montreal, Canada), Mark Bastin (Centre for Cognitive
Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK), Ian J. Deary (Centre for
Cognitive Epidemiology and Cognitive Ageing, University of Edinburgh, Edinburgh, UK), Veronica Witte
(Department of Neurology, Max Planck Institute of Cognitive and Brain Sciences, Leipzig, Germany), Frauke
Beyer (Department of Neurology, Max Planck Institute of Cognitive and Brain Sciences, Leipzig, Germany),
Markus Loeffler (Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig,
Germany), Karen A. Mather (Centre for Healthy Brain Ageing,School of Psychiatry, University of New South
Wales, Sydney, Australia), Peter R. Schofield (Neuroscience Research Australia, Sydney, Australia),
Anbupalam Thalamuthu (Centre for Healthy Brain Ageing,School of Psychiatry, University of New South
Wales, Sydney, Australia), John B. Kwok (School of Medical Sciences, University of New South Wales,
Sydney, Australia), Margaret J. Wright (Queensland Brain Institute, The University of Queensland, St Lucia,
QLD, Australia), David Ames (National Ageing Research Institute, Royal Melbourne Hospital, Victoria,
Australia), Julian Trollor (Centre for Healthy Brain Ageing,School of Psychiatry, University of New South Wales,
Sydney, Australia), Jiyang Jiang (Centre for Healthy Brain Ageing,School of Psychiatry, University of New
South Wales, Sydney, Australia), Henry Brodaty (Dementia Centre for Research Collaboration, University of
New South Wales, Sydney, NSW, Australia), Wei Wen (Centre for Healthy Brain Ageing,School of Psychiatry,
University of New South Wales, Sydney, Australia), Meike W Vernooi (Department of Radiology and Nuclear
Medicine, Erasmus MC, Rotterdam, The Netherlands), Albert Hofman (Department of Epidemiology, Harvard
T.H. Chan School of Public Health, Boston, MA, USA), André G. Uitterlinden (Department of Epidemiology,
Erasmus MC, Rotterdam, The Netherlands), Wiro J. Niessen (Imaging Physics, Faculty of Applied Sciences,
Delft University of Technology, The Netherlands), Katharina Wittfeld (German Center for Neurodegenerative
Diseases (DZNE), Site Rostock/Greifswald, Germany), Robin Bülow (Institute for Diagnostic Radiology and
Neuroradiology, University Medicine Greifswald, Greifswald, Germany), Zdenka Pausova (Hospital for Sick
Children, Toronto, Canada), Bruce Pike (Departments of Radiology and Neurology, University of Calgary,
Calgary, Canada), Sophie Maingault (University of Bordeaux, Institut des Maladies Neurodégénratives, CNRS,
Ubordeaux, Bordeaux, France), Bernard Mazoyer (University of Bordeaux, Institut des Maladies
Neurodégénratives, CNRS, Ubordeaux, Bordeaux, France), Michael C. Neale (Virginia Institute for Psychiatric
and Behavior Genetics, Virginia Commonwealth University, VA, USA), Carol E. Franz (Department of
Psychiatry, University of California San Diego, CA, USA), Michael J. Lyons (Department of Psychological and
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Brain Sciences, Boston University, Boston, MA, USA), Matthew S. Panizzon (Department of Psychiatry,
University of California San Diego, CA, USA), Mark Logue (National Center for PTSD at Boston VA Healthcare
System, Boston, MA, USA), Perminder S. Sachdev (Centre for Healthy Brain Ageing, School of Psychiatry,
University of New South Wales, Sydney, Australia), William S. Kremen (Department of Psychiatry, University
of California San Diego, CA, USA), Joanna A. Wardlaw (Centre for Cognitive Epidemiology and Cognitive
Ageing, University of Edinburgh, Edinburgh, UK), Arno Villringer (Department of Neurology, Max Planck
Institute of Cognitive and Brain Sciences, Leipzig, Germany), Cornelia M. van Duijn (Department of
Epidemiology, Erasmus MC, Rotterdam, The Netherlands), Hans Jörgen Grabe (Department of Psychiatry
and Psychotherapy, University Medicine Greifswald, Germany), WT. Longstreth Jr (Departments of Neurology
and Epidemiology, University of Washington, Seattle, WA, USA), Myriam Fornage (Institute of Molecular
Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX,
USA), Tomas Paus (Rotman Research Institute, Baycrest. Toronto, Canada), Stephanie Debette (University
of Bordeaux, Bordeaux Population Health Research Center, INSERM, Bordeaux, France), M. Arfan Ikram
(Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands), Helena Schmidt
(Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Medical University of Graz, Graz,
Austria), Reinhold Schmidt (Clinical Division of Neurogeriatrics, Department of Neurology, Medical University
of Graz, Graz, Austria), Sudha Seshadri (Department of Epidemiology and Biostatistics, Glenn Biggs Institute
for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, USA)
EPIGEN Consortium: David B. Goldstein (The Centre for Genomics and Population Genetics, Duke
University Institute for Genome Sciences and Policy, Durham, North Carolina, USA), Erin L. Heinzen (The
Centre for Genomics and Population Genetics, Duke University Institute for Genome Sciences and Policy,
Durham, North Carolina, USA), Kevin Shianna (The Centre for Genomics and Population Genetics, Duke
University Institute for Genome Sciences and Policy, Durham, North Carolina, USA), Rodney Radtke
(Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA) and Ruth Ottmann
(Departments of Epidemiology, Neurology, and the G.H. Sergievsky Center, Columbia University, New York,
NY).
IMAGEN Consortium: Dr. Eric Artiges (INSERM), Semiha Aydin (Physikalisch-Technische Bundesanstalt),
Prof. Dr. Dr. Tobias Banaschewski (Central Institute of Mental Health), Alexis Barbot (Commissariat à l'Energie
Atomique), Prof. Dr. Gareth Barker (King's College London), Andreas Becker (Georg-August-Universität
Göttingen), Pauline Bezivin-Frere (INSERM), Dr. Francesca Biondo (King's College London), Dr. Arun Bokde
(Trinity College Dublin), Uli Bromberg (University of Hamburg), Dr. Ruediger Bruehl, Prof. Dr. Christian Büchel
(University of Hamburg), Dr. Congying Chu (King's College London), Dr. Patricia Conrod (King's College
London), Laura Daedelow (Charité Universitätsmedizin Berlin), Dr. Jeffrey Dalley (Cambridge University), Dr.
Sylvane Desrivieres (King's College London), Eoin Dooley (Trinity College Dublin), Irina Filippi (INSERM), Dr
Ariane Fillmer (Physikalisch-Technische Bundesanstalt ), Prof. Dr. Herta Flor (Central Institute of Mental
Health), Juliane Fröhner (Technische Universität Dresden ), Vincent Frouin (Commissariat à l'Energie
Atomique), Dr. Hugh Garavan (University of Vermont), Prof. Penny Gowland (University of Nottingham),
Yvonne Grimmer (Central Institute of Mental Health), Prof. Dr. Andreas Heinz (Charité Universitätsmedizin
Berlin), Dr. Sarah Hohmann (Central Institute of Mental Health), Albrecht Ihlenfeld (Physikalisch-Technische
Bundesanstalt ), Alex Ing (King's College London), Corinna Isensee (University Medical Center Göttingen ),
Dr. Bernd Ittermann (Physikalisch-Technische Bundesanstalt ), Dr. Tianye Jia (King's College London), Dr.
Hervé Lemaitre (INSERM), Emma Lethbridge (University of Nottingham), Prof. Dr. Jean-Luc Martinot
(INSERM), Sabina Millenet (Central Institute of Mental Health), Sarah Miller (Charité Universitätsmedizin
Berlin), Ruben Miranda (INSERM), PD Dr. Frauke Nees (Central Institute of Mental Health), Dr. Marie-Laure
Paillere (INSERM), Dimitri Papadopoulos (INSERM), Prof. Dr. Tomáš Paus (Bloorview Research Institute,
Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychatry, University of
Toronto), Dr. Zdenka Pausova (University of Toronto), Dr. Dr. Jani Pentilla (INSERM), Dr. Jean-Baptiste Poline
(Commissariat à l'Energie Atomique), Prof. Dr. Luise Poustka (University Medical Center Göttingen ), Dr. Erin
Burke Quinlan (King's College London), Dr. Michael Rapp (Charité Universitätsmedizin Berlin), Prof. Dr. Trevor
Robbins (Cambridge University), Dr. Gabriel Robert (King's College London), John Rogers (Delosis), Dr.
Barbara Ruggeri (King's College London), Prof. Dr. Gunter Schumann (King's College London), Prof. Dr.
Michael Smolka (Technische Universität Dresden), Argyris Stringaris (National Institute of Mental Health),
Betteke van Noort (Charité Universitätsmedizin Berlin), Dr. Henrik Walter (Charité Universitätsmedizin Berlin),
Dr. Robert Whelan (Trinity College Dublin), Prof. Dr. Steve Williams (King's College London).
Parkinson’s Progression Markers Initiative (PPMI): Data used in preparing this article were obtained from
the PPMI database (http://www.ppmi-info.org/). As such, many investigators within the PPMI contributed to the
design and implementation of PPMI and/or provided data but did not participate in analysis or writing of this
report. A complete listing of PPMI investigators may be found at: http://www.ppmi-info.org/authorslist/. Kenneth
Marek (Institute for Neurodegenerative Disorders, New Haven), Danna Jennings (Institute for
Neurodegenerative Disorders, New Haven), Shirley Lasch (Institute for Neurodegenerative Disorders, New
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
32
Haven), Caroline Tanner (University of California, San Francisco), Tanya Simuni (Northwestern University,
Chicago), Christopher Coffey (University of Iowa, Iowa City), Karl Kieburtz (Clinical Trials Coordination Center,
University of Rochester), Renee Wilson (Clinical Trials Coordination Center, University of Rochester), Werner
Poewe (Innsbruck Medical University, Innsbruck), Brit Mollenhauer (Paracelsus-Elena Klinik, Kassel), Douglas
Galasko (University of California, San Diego), Tatiana Foroud (Indiana University, Indianapolis), Todd Sherer
(The Michael J. Fox Foundation for Parkinson's Research, New York), Sohini Chowdhury (The Michael J. Fox
Foundation for Parkinson's Research, New York), Mark Frasier (The Michael J. Fox Foundation for Parkinson's
Research, New York), Catherine Kopil (The Michael J. Fox Foundation for Parkinson's Research, New York),
Vanessa Arnedo (The Michael J. Fox Foundation for Parkinson's Research, New York), Alice Rudolph (Clinical
Trials Coordination Center, University of Rochester), Cynthia Casaceli (Clinical Trials Coordination Center,
University of Rochester), John Seibyl (Institute for Neurodegenerative Disorders, New Haven), Susan Mendick
(Institute for Neurodegenerative Disorders, New Haven), Norbert Schuff (University of California, San
Francisco), Chelsea Caspell (University of Iowa, Iowa City), Liz Uribe (University of Iowa, Iowa City), Eric
Foster (University of Iowa, Iowa City), Katherine Gloer (University of Iowa, Iowa City), Jon Yankey (University
of Iowa, Iowa City), Arthur Toga (Laboratory of Neuroimaging (LONI), University of Southern California), Karen
Crawford (Laboratory of Neuroimaging (LONI), University of Southern California), Paola Casalin (BioRep,
Milan), Giulia Malferrari (BioRep, Milan), Andrew Singleton (National Institute on Aging, NIH, Bethesda), Keith
A. Hawkins (Yale University, New Haven), David Russell (Institute for Neurodegenerative Disorders, New
Haven), Stewart Factor (Emory University of Medicine, Atlanta), Penelope Hogarth (Oregon Health and
Science University, Portland), David Standaert (University of Alabama at Birmingham, Birmingham), Robert
Hauser (University of South Florida, Tampa), Joseph Jankovic (Baylor College of Medicine, Houston), Matthew
Stern (University of Pennsylvania, Philadelphia), Lama Chahine (University of Pennsylvania, Philadelphia),
James Leverenz (University of Washington, Seattle), Samuel Frank (Boston University, Boston), Irene Richard
(University of Rochester, Rochester), Klaus Seppi (Innsbruck Medical University, Innsbruck), Holly Shill
(Banner Research Institute, Sun City), Hubert Fernandez (Cleveland Clinic, Cleveland), Daniela Berg
(University of Tuebingen, Tuebingen), Isabel Wurster (University of Tuebingen, Tuebingen), Zoltan Mari (Johns
Hopkins University, Baltimore), David Brooks (Imperial College of London, London), Nicola Pavese (Imperial
College of London, London), Paolo Barone (University of Salerno, Salerno), Stuart Isaacson (Parkinson’s
Disease and Movement Disorders Center, Boca Raton), Alberto Espay (University of Cincinnati, Cincinnati),
Dominic Rowe (Macquarie University, Sydney), Melanie Brandabur (The Parkinson's Institute, Sunnyvale),
James Tetrud (The Parkinson's Institute, Sunnyvale), Grace Liang (The Parkinson's Institute, Sunnyvale), Alex
Iranzo (Hospital Clinic of Barcelona, Barcelona), Eduardo Tolosa (Hospital Clinic of Barcelona, Barcelona),
Shu-Ching Hu (University of Washington, Seattle), Gretchen Todd (University of Washington, Seattle), Laura
Leary (Institute for Neurodegenerative Disorders, New Haven), Cheryl Riordan (Institute for
Neurodegenerative Disorders, New Haven), Linda Rees (The Parkinson's Institute, Sunnyvale), Alicia Portillo
(Oregon Health and Science University, Portland), Art Lenahan (Oregon Health and Science University,
Portland), Karen Williams (Northwestern University, Chicago), Stephanie Guthrie (University of Alabama at
Birmingham, Birmingham), Ashlee Rawlins (University of Alabama at Birmingham, Birmingham), Sherry
Harlan (University of South Florida, Tampa), Christine Hunter (Baylor College of Medicine, Houston), Baochan
Tran (University of Pennsylvania, Philadelphia), Abigail Darin (University of Pennsylvania, Philadelphia), Carly
Linder (University of Pennsylvania, Philadelphia), Marne Baca (University of Washington, Seattle), Heli Venkov
(University of Washington, Seattle), Cathi-Ann Thomas (Boston University, Boston), Raymond James (Boston
University, Boston), Cheryl Deeley (University of Rochester, Ro
chester), Courtney Bishop (University of
Ro
chester, Rochester), Fabienne Sprenger (Innsbruck Medical University, Innsbruck), Diana Willeke
(Paracelsus-Elena Klinik, Kassel), Sanja Obradov (Banner Research Institute, Sun City), Jennifer Mule
(Cleveland Clinic, Cleveland), Nancy Monahan (Cleveland Clinic, Cleveland), Katharina Gauss (University of
Tuebingen, Tuebingen), Deborah Fontaine (University of California, San Diego), Christina Gigliotti (University
of California, San Diego), Arita McCoy (Johns Hopkins University, Baltimore), Becky Dunlop (Johns Hopkins
University, Baltimore), Bina Shah (Imperial College of London, London), Susan Ainscough (University of
Salerno, Salerno), Angela James (Parkinson’s Disease and Movement Disorders Center, Boca Raton),
Rebecca Silverstein (Parkinson’s Disease and Movement Disorders Center, Boca Raton), Kristy Espay
(University of Cincinnati, Cincinnati), Madelaine Ranola (Macquarie University, Sydney), Thomas Comery
(Pfizer, Inc., Groton), Jesse Cedarbaum (Biogen Idec, Cambridge), Bernard Ravina (Biogen Idec, Cambridge),
Igor D. Grachev (GE Healthcare, Princeton), Jordan S. Dubow (AbbVie, Abbot Park), Michael Ahlijanian
(Bristol-Myers Squibb Company), Holly Soares (Bristol-Myers Squibb Company), Suzanne Ostrowizki
(F.Hoffmann La-Roche, Basel), Paulo Fontoura (F.Hoffmann La-Roche, Basel), Alison Chalker (Merck & Co.,
North Wales), David L. Hewitt (Merck & Co., North Wales), Marcel van der Brug (Genentech, Inc., South San
Francisco), Alastair D. Reith (GlaxoSmithKline, Stevenage), Peggy Taylor (Covance, Dedham), Jan Egebjerg
(H. Lundbeck), Mark Minton (Avid Radiopharmaceuticals, Philadelphia ), Andrew Siderowf (Avid
Radiopharmaceuticals, Philadelphia ), Pierandrea Muglia (UCB Pharma S.A., Brussels), Robert Umek (Meso
Scale Discovery), Ana Catafau (Meso Scale Discovery), Vera Kiyasova (Servier), Barbara Saba (Servier).
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
33
SYS Consortium: Tomáš Paus MD PhD (Bloorview Research Institute, University of Toronto, Canada),
Zdenka Pausova, MD (The Hospital for Sick Children, University of Toronto, Canada), G. Bruce Pike PhD
(Department of Radiology, University of Calgary, Canada), Louis Richer PhD (Department of Health Sciences,
University of Quebec in Chicoutimi, Canada), Gabriel Leonard PhD (Montreal Neurological Institute, McGill
University, Canada), Michel Perron PhD (CEGEP Jonquiere, Canada), Suzanne Veillette PhD (CEGEP
Jonquiere, Canada) and Manon Bernard BComp(The Hospital for Sick Children, University of Toronto,
Canada).
Additional cohort information
ADNI
Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative
database. The ADNI was launched in 2003 as a 5-year public–private partnership to assess and optimize
biomarkers for clinical trials in Alzheimer’s disease. The initial sample included older adults who were cognitive
normal (CN) as well as meeting criteria for MCI and clinical AD. In 2011, ADNI-2 began to recruit an additional
CN group as well as individuals with significant memory concerns (SMC), early MCI and late MCI,and AD. .
These subjects, and others carried forward from ADNI-1, were scanned with an updated neuroimaging
protocol. Participants were recruited from over 60 sites across the U.S. and Canada. For up-to-date
information, please see www.adni-info.org.
ALSPAC
Pregnant women resident in Avon, UK with expected dates of delivery 1st April 1991 to 31st December 1992
were invited to take part in the study. The initial number of pregnancies enrolled is 14,541 (for these at least
one questionnaire has been returned or a “Children in Focus” clinic had been attended by 19/07/99). Of these
initial pregnancies, there was a total of 14,676 fetuses, resulting in 14,062 live births and 13,988 children who
were alive at 1 year of age. When the oldest children were approximately 7 years of age, an attempt was made
to bolster the initial sample with eligible cases who had failed to join the study originally. As a result, when
considering variables collected from the age of seven onwards (and potentially abstracted from obstetric notes)
there are data available for more than the 14,541 pregnancies mentioned above. The number of new
pregnancies not in the initial sample (known as Phase I enrolment) that are currently represented on the built
files and reflecting enrolment status at the age of 18 is 706 (452 and 254 recruited during Phases II and III
respectively), resulting in an additional 713 children being enrolled. The phases of enrolment are described in
more detail in the cohort profile paper (see footnote 4 below). The total sample size for analyses using any
data collected after the age of seven is therefore 15,247 pregnancies, resulting in 15,458 fetuses. Of this total
sample of 15,458 fetuses, 14,775 were live births and 14,701 were alive at 1 year of age. A 10% sample of
the ALSPAC cohort, known as the Children in Focus (CiF) group, attended clinics at the University of Bristol
at various time intervals between 4 to 61 months of age. The CiF group were chosen at random from the last
6 months of ALSPAC births (1432 families attended at least one clinic). Excluded were those mothers who
had moved out of the area or were lost to follow-up, and those partaking in another study of infant development
in Avon. The data used in the present study were collected from 391 males and further description of this
subset and the variables used in this study are provided in Supplementary Tables 2-4.
The study website contains details of all the data that is available through a fully searchable data dictionary
(http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/).
Further information can be found in the following papers:
Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J, Molloy L, Ness A, Ring S, Davey Smith G.
Cohort Profile: The ‘Children of the 90s’; the index offspring of The Avon Longitudinal Study of Parents and
Children (ALSPAC). International Journal of Epidemiology 2013; 42: 111-127;
Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Davey Smith G, Henderson J, Macleod J, Molloy
L, Ness A, Ring S, Nelson SM, Lawlor DA. Cohort Profile: The Avon Longitudinal Study of Parents and
Children: ALSPAC mothers cohort. International Journal of Epidemiology 2013; 42:97-110;
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Acknowledgements
ENIGMA: The study was supported in part by grant U54 EB020403 from the NIH Big Data to Knowledge
(BD2K) Initiative, a cross-NIH partnership. Additional support was provided by R01MH116147, P41 EB015922,
RF1AG051710, RF1 AG041915 (to P.T.), by P01 AG026572, R01 AG059874 and by R01 MH117601 (to N.J.
and L.S.). S.E.M. was funded by an NHMRC Senior Research Fellowship (APP1103623). L.C.-C. was
supported by a QIMR Berghofer Fellowship.
1000BRAINS: Is a population-based cohort based on the Heinz-Nixdorf Recall Study and is supported in part
by the German National Cohort. We thank the Heinz Nixdorf Foundation (Germany) for their generous support
in terms of the Heinz Nixdorf Study. The HNR study is also supported by the German Ministry of Education
and Science (FKZ 01EG940), and the German Research Council (DFG, ER 155/6-1). The authors are
supported by the Initiative and Networking Fund of the Helmholtz Association (Svenja Caspers) and the
European Union's Horizon 2020 Research and Innovation Programme under Grant Agreements 720270
(Human Brain Project SGA1; Sven Cichon) and 785907 (Human Brain Project SGA2; Svenja Caspers, Sven
Cichon). This work was further supported by the German Federal Ministry of Education and Research (BMBF)
through the Integrated Network IntegraMent (Integrated Understanding of Causes and Mechanisms in Mental
Disorders) under the auspices of the e:Med Program (grant 01ZX1314A; Sven Cichon), and by the Swiss
National Science Foundation (SNSF, grant 156791; Sven Cichon).
ADNI1 and ADNI2GO: Data used in the preparation of this article were obtained from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-
private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been
to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other
biological markers, and clinical and neuropsychological assessment can be combined to measure the
progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD). Data collection and
sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National
Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-
12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and
Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association;
Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb
Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun;
F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.;
Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical
Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.;
NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal
Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of
Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are
facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is
the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s
Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the
Laboratory for Neuro Imaging at the University of Southern California. Samples from the National Centralized
Repository for Alzheimer's Disease and Related Dementias (NCRAD), which receives government support
under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were
used in this study. We thank contributors who collected samples used in this study, as well as patients and
their families, whose help and participation made this work pos
sible. Additional support for data analysis was
provide
d by NLM R01 LM012535 and NIA R03 AG054936 (to K.N.).
ALSPAC: We are extremely grateful to all the families who took part in this study, the midwives for their help
in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory
technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. Ethical
approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research
Ethics Committees. The UK Medical Research Council and Wellcome (Grant ref: 102215/2/13/2) and the
University of Bristol provide core support for ALSPAC. This publication is the work of the authors and they will
serve as guarantors for the contents of this paper. A comprehensive list of grants funding is available on the
ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf).
ALSPAC neuroimaging data was specifically funded by RO1 MH085772 (Axon, Testosterone and Mental
Health during Adolescence; PI: T. Paus). GWAS data was generated by Sample Logistics and Genotyping
Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from
23andMe. We would like to acknowledge the help of Lara B Clauss during the quality control process of the
ALSPAC neuroimaging data.
BETULA: This work was supported by a Wallenberg Scholar grant from the Knut and Alice Wallenberg (KAW)
Foundation and a grant from Torsten and Ragnar Söderbergs Foundation to LN, a grant from HelseVest RHF
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
36
(Grant 911554) to SLH, grants from the Bergen Research Foundation and the University of Bergen to SLH,
grants from the Dr Einar Martens Fund and the K.G. Jebsen Foundation to SLH and VMS, the Research
Council of Norway to TE (Grant 177458/V 50) and LTW (Grant 204966/F 20). We thank the Centre for
Advanced Study (CAS) at the Norwegian Academy of Science and Letters in Oslo for hosting collaborative
projects and workshops between Norway and Sweden in 2011–2012. Image analyses were performed on
resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N in Umeå.
BIG: The Brain Imaging Genetics (BIG) database was established in Nijmegen, the Netherlands in 2007. This
resource is now part of Cognomics, a joint initiative by researchers of the Donders Centre for Cognitive
Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud University
Medical Center, and the Max Planck Institute for Psycholinguistics. The present study includes two subsamples
of BIG, from successive waves of genotyping on Affymetrix (BIG-Affy) and PsychChip (BIG-PsychChip) arrays.
Analyses for this project were carried out on the Dutch national e-infrastructure with the support of SURF
Cooperative. Nijmegen’s BIG resource is part of Cognomics, a joint initiative by researchers of the Donders
Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the
Radboud University Medical Center, and the Max Planck Institute for Psycholinguistics (funded by the Max
Planck Society). Support for the Cognomics Initiative, including phenotyping and genotyping of BIG cohorts,
comes from funds of the participating departments and centres and from external national grants, i.e. the
Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL), the
Hersenstichting Nederland, and the Netherlands Organisation for Scientific Research (NWO), including the
NWO Brain & Cognition Excellence Program (grant 433-09-229) and the Vici Innovation Program (grant 016-
130-669 to BF). Additional support was received from the European Community’s Seventh Framework
Programme (FP7/2007–2013) under grant agreements n◦ 602805 (Aggressotype), n◦ 602450 (IMAGEMEND),
and n◦ 278948 (TACTICS) as well as from the European Community’s Horizon 2020 Programme
(H2020/2014–2020) under grant agreements n◦ 643051 (MiND) and n◦ 667302 (CoCA) and and from the
Innovative Medicines Initiative (IMI) 2 Joint Undertaking (H2020/EFPIA) under grant agreement no. 115916
(PRISM). The work was also supported by grants for the ENIGMA Consortium (Foundation for the National
Institutes of Health (NIH); grant number U54 EB020403) from the BD2K Initiative of a cross-NIH partnership.
BONN: The authors would like to thank (in alphabetical order) Marcel Bartling, Ulrike Broicher, Laura
Ehrmantraut, Anna Maaser, Bettina Mahlow, Stephanie Mentges, Karolina Raczka, Laura Schinabeck, and
Peter Trautner for their support and help. The study was partly funded by the Frankfurt Institute for Risk
Management and Regulation (FIRM) and BW was supported by a Heisenberg Grant of the German Research
Foundation ((Deutsche Forschungsgemeinschaft (DFG)), WE 4427(3-2)).
BrainScale: This work was supported by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO
51.02.061 to H.H., NWO 51.02.062 to D.B., NWO-NIHC Programs of excellence 433-09-220 to H.H., NWO-
MagW 480-04-004 to D.B., and NWO/SPI 56-464-14192 to D.B.); FP7 Ideas: European Research Council
(ERC-230374 to D.B.); and Universiteit Utrecht (High Potential Grant to H.H.).
CARDIFF: We are grateful to all researchers within Cardiff University who contributed to the MBBrains panel
and to NCMH for their support with genotyping. NCMH supported genotyping.
CHARGE: Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and
Blood Institute grant HL105756 and for the neuroCHARGE phenotype working group through the National
Institute on Aging grant AG033193. Atherosclerosis Risk in Communities Study (ARIC): The Atherosclerosis
Risk in Communities study was performed as a collaborative study supported by National Heart, Lung, and
Blood Institute (NHLBI) contracts (HHSN268201100005C, HSN268201100006C, HSN268201100007C,
HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and
HHSN268201100012C), R01HL70825, R01HL087641, R01HL59367, and R0
1HL086694; National Human
Genom
e Research Institute contract U01HG004402; and National Institutes of Health (NIH) contract
HHSN268200625226C. Infrastructure was partly supported by grant No. UL1RR025005, a component of the
NIH and NIH Roadmap for Medical Research. This project was partially supported by National Institutes of
Health R01 grants HL084099 and NS087541 to MF. Austrian Stroke Prevention Family (ASPS) / Austrian
Stroke Prevention Family Study: The authors thank the staff and the participants for their valuable
contributions. We thank Birgit Reinhart for her long-term administrative commitment, Elfi Hofer for the technical
assistance at creating the DNA bank, Ing. Johann Semmler and Anita Harb for DNA sequencing and DNA
analyses by TaqMan assays and Irmgard Poelzl for supervising the quality management processes after
ISO9001 at the biobanking and DNA analyses. The Medical University of Graz and the Steiermärkische
Krankenanstaltengesellschaft support the databank of the ASPS/ASPS-Fam. The research reported in this
article was funded by the Austrian Science Fund (FWF) grant numbers PI904, P20545-P05 and P13180 and
supported by the Austrian National Bank Anniversary Fund, P15435 and the Austrian Ministry of Science under
the aegis of the EU Joint Programme-Neurodegenerative Disease Research (JPND)-www.jpnd.eu.
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
37
Cardiovascular Health Study (CHS): This CHS research was supported by NHLBI contracts
HHSN268201200036C, HHSN268200800007C, N01HC55222, N01HC85079, N01HC85080, N01HC85081,
N01HC85082, N01HC85083, N01HC85086; and NHLBI grants U01HL080295, R01HL087652,
R01HL105756, R01HL103612, R01HL120393, and R01HL130114 with additional contribution from the
National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided through
R01AG023629, R01AG15928, and R01AG033193 from the National Institute on Aging (NIA). A full list of
principal CHS investigators and institutions can be found at CHS-NHLBI.org. The provision of genotyping data
was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR000124,
and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC)
grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The content is solely
the responsibility of the authors and does not necessarily represent the official views of the National Institutes
of Health. Erasmus Rucphen Family Study (ERF): Erasmus Rucphen Family (ERF) was supported by the
Consortium for Systems Biology (NCSB), both within the framework of the Netherlands Genomics Initiative
(NGI)/Netherlands Organisation for Scientific Research (NWO). ERF study as a part of EUROSPAN (European
Special Populations Research Network) was supported by European Commission FP6 STRP grant number
018947 (LSHG-CT-2006-01947) and also received funding from the European Community’s Seventh
Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European
Commission under the programme “Quality of Life and Management of the Living Resources” of 5th
Framework Programme (No. QLG2-CT-2002-01254) as well as FP7 project EUROHEADPAIN (nr602633).
High-throughput analysis of the ERF data was supported by joint grant from Netherlands Organisation for
Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). High
throughput metabolomics measurements of the ERF study has been supported by BBMRI-NL (Biobanking and
Biomolecular Resources Research Infrastructure Netherlands). Framingham Heart Study (FHS): This work
was supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-
HC-25195 and No. HHSN268201500001I) and its contract with Affymetrix, Inc. for genotyping services
(Contract No. N02-HL-6-4278). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-
II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School
of Medicine and Boston Medical Center. This study was also supported by grants from the National Institute
of Aging (R01s AG033040, AG033193, AG054076, AG049607, AG008122, AG016495; and U01-AG049505)
and the National Institute of Neurological Disorders and Stroke (R01-NS017950). We would like to thank the
dedication of the Framingham Study participants, as well as the Framingham Study team, especially
investigators and staff from the Neurology group, for their contributions to data collection. Dr. DeCarli is
supported by the Alzheimer’s Disease Center (P30 AG 010129). The views expressed in this manuscript are
those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute;
the National Institutes of Health; or the U.S. Department of Health and Human Services. Lothian Birth Cohort
1936 (LBC1936): This project is funded by the Age UK’s Disconnected Mind programme
(http://www.disconnectedmind.ed.ac.uk) and also by Research Into Ageing (Refs. 251 and 285). The whole
genome association part of the study was funded by the Biotechnology and Biological Sciences Research
Council (BBSRC; Ref. BB/F019394/1). Analysis of the brain images was funded by the Medical Research
Council Grants G1001401 and 8200. The imaging was performed at the Brain Research Imaging Centre, The
University of Edinburgh (http://www.bric.ed.ac.uk), a centre in
the SINAPSE Collaboration
(http://www.s
inapse.ac.uk). The work was undertaken by The University of Edinburgh Centre for Cognitive
Ageing and Cognitive Epidemiology (http://www.ccace.ed.ac.uk), part of the cross council Lifelong Health and
Wellbeing Initiative (Ref. G0700704/84698). Funding from the BBSRC, Engineering and Physical Sciences
Research Council (EPSRC), Economic and Social Research Council (ESRC), Medical Research Council
(MRC) and Scottish Funding Council through the SINAPSE Collaboration is gratefully acknowledged. We
thank the LBC1936 participants and research team members. We also thank the nurses and staff at the
Wellcome Trust Clinical Research Facility (http://www.wtcrf.ed.ac.uk), where subjects were tested and the
genotyping was performed. LIFE-Adult: LIFE-Adult is funded by the Leipzig Research Center for Civilization
Diseases (LIFE). LIFE is an organizational unit affiliated to the Medical Faculty of the University of Leipzig.
LIFE is funded by means of the European Union, by the European Regional Development Fund (ERDF) and
by funds of the Free State of Saxony within the framework of the excellence initiative. This work was also
funded by the Deutsche Forschungsgemeinschaft (Grant Number: CRC 1052 “Obesity mechanisms” project
A1 to AV) and by the Max Planck Society. Sydney Memory and Ageing Study (MAS): MAS is funded by the
Australian National Health and Medical Research Council (NHMRC)/Australian Research Council Strategic
Award (Grant 401162), NHMRC Project grant 1405325. We would like to gratefully acknowledge and thank
the Sydney MAS participants and supporters and the Sydney MAS Research Team. Older Australian Twin
Study (OATS): OATS is funded by the Australian National Health and Medical Research Council
(NHMRC)/Australian Research Council Strategic Award (Grant 401162) , NHMRC Program Grants (350833,
568969, 109308) We would like thank and gratefully acknowledge the OATS participants, their supporters and
the OATS Research Team. Rotterdam Study (RSI, RSII, RSIII): The Rotterdam Study is funded by Erasmus
Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and
Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education,
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
38
Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the
Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam
Study and the participating general practitioners and pharmacists. The generation and management of GWAS
genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility
of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
The GWAS datasets are supported by the Netherlands Organisation of Scientific Research NWO Investments
(nr. 175.010.2005.011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus
MC, the Research Institute for Diseases in the Elderly (014-93-015; RIDE2), the Netherlands Genomics
Initiative (NGI)/Netherlands Organisation for Scientific Research (NWO) Netherlands Consortium for Healthy
Aging (NCHA), project nr. 050-060-810. We thank Pascal Arp, Mila Jhamai, Marijn Verkerk, Lizbeth Herrera
and Marjolein Peters, and Carolina Medina-Gomez, for their help in creating the GWAS database, and Karol
Estrada, Yurii Aulchenko, and Carolina Medina-Gomez, for the creation and analysis of imputed data. This
work has been performed as part of the CoSTREAM project (www.costream.eu) and has received funding
from the European Union's Horizon 2020 research and innovation programme under grant agreement No
667375. Study of Health in Pomerania (SHIP) / Study of Health in Pomerania Trend (SHIP- Trend): SHIP is
part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by
the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the
Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania,
and the network ‘Greifswald Approach to Individualized Medicine (GANI_MED)’ funded by the Federal Ministry
of Education and Research (grant 03IS2061A). Genome-wide data have been supported by the Federal
Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers,
Erlangen, Germany and the Federal State of Mecklenburg- West Pomerania. Whole-body MR imaging was
supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of
Mecklenburg West Pomerania. The University of Greifswald is a member of the Caché Campus program of
the InterSystems GmbH. Saguenay Youth Study (SYS): The Saguenay Youth Study has been funded by the
Canadian Institutes of Health Research (TP, ZP), Heart and Stroke Foundation of Canada (ZP), and the
Canadian Foundation for Innovation (ZP). We thank all families who took part in the Saguenay Youth Study.
SYS is supported by the Canadian Institutes of Health Research: NET54015, NRF86678, TMH109788. Three-
City Dijon (3C-Dijon): The Three City (3C) Study is conducted under a partnership agreement among the
Institut National de la Santé et de la Recherche Médicale (INSERM), the University of Bordeaux, and Sanofi-
Aventis. The Fondation pour la Recherche Médicale funded the preparation and initiation of the study. The 3C
Study is also supported by the Caisse Nationale Maladie des Travailleurs Salariés, Direction Générale de la
Santé, Mutuelle Générale de l’Education Nationale (MGEN), Institut de la Longévité, Conseils Régionaux of
Aquitaine and Bourgogne, Fondation de France, and Ministry of Research–INSERM Programme “Cohortes et
collections de données biologiques.” Christophe Tzourio and Stéphanie Debette have received investigator-
initiated research funding from the French National Research Agency (ANR) and from the Fondation Leducq.
Stéphanie Debette is supported by a starting grant from the European Research Council (SEGWAY), a grant
from the Joint Programme of Neurodegenerative Disease research (BRIDGET), and the Initiative of Excellence
of Bordeaux University. We thank Dr. Anne Boland (CNG) for her technical help in preparing the DNA samples
for analyses. This work was supported by the National Foundation for Alzheimer’s disease and related
disorders, the Institut Pasteur de Lille, the labex DISTALZ and the Centre National de Génotypage. Vietnam
Era Twin Study of Aging (VETSA): United States National Institute of Health VA San Diego Center of
Excellence for Stress and Mental Health R00DA023549; DA-18673;
NIA R01 AG018384; R01 AG018386;
R01 AG022381; R01 AG022982; R01 DA025109 05; R01 HD050735; K08 AG047903; R03 AG 046413; U54
EB020403; and R01 HD050735-01A2
CLING: The GIG (Genomic Imaging Göttingen) sample was established at the Center for Translational
Research in Systems Neuroscience and Psychiatry (Head: Prof. Dr. O. Gruber) at Göttingen University. We
thank Maria Keil, Esther Diekhof, Tobias Melcher and Ilona Henseler for assistance in data acquisition. We
are grateful to all persons who kindly participated in the GIG study.
DNS: We thank the Duke Neurogenetics Study participants and the staff of the Laboratory of NeuroGenetics.
The Duke Neurogenetics Study received support from Duke University as well as US-National Institutes of
Health grants R01DA033369 and R01DA031579.
EPIGEN: Work from the London Cohort was supported by research grants from the Wellcome Trust (grant
084730 to S.M.S.), University College London (UCL)/University College London Hospitals (UCLH) NIHR
Biomedical Research Centre/Specialist Biomedical Research Centres (CBRC/SBRC) (grant 114 to S.M.S.),
the Comprehensive Local Research Network (CLRN) Flexibility and Sustainability Funding (FSF) (grant
CEL1300 to S.M.S.), The Big Lottery Fund, the Wolfson Trust and the Epilepsy Society. This work was partly
undertaken at UCLH/UCL, which received a proportion of funding from the UK Department of Health’s NIHR
Biomedical Research Centres funding scheme.
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
39
FBIRN: We are thankful to Mrs. Liv McMillan, BS for overall study coordination, Harry Mangalam, PhD, Joseph
Farran, BS, and Adam Brenner, BS, for administering the University of California, Irvine High-Performance
Computing cluster, and to the research subjects for their participation. This work was supported by the National
Center for Research Resources at the National Institutes of Health [grant numbers: NIH 1 U24 RR021992
(Function Biomedical Informatics Research Network), NIH 1 U24 RR025736-01 (Biomedical Informatics
Research Network Coordinating Center)], the National Center for Research Resources and the National
Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1 TR000153, and
the National Institutes of Health through 5R01MH094524, and P20GM103472.
FOR2107: This work was funded by the German Research Foundation (DFG, grant FOR2107 DA1151/5-1
and DA1151/5-2 to UD; JA1890/7-1, JA1890/7-2 to AJ; KI 588/14-1, KI 588/14-2 to TK; KR 3822/7-1, KR
3822/7-2 to AK; NO246/10-1, NO246/10-2 to MMN).
Frontal-temporal dementia GWAS (utlised to calculate the genetic correlations). We acknowledge the
investigators of the original study (Ferrari et al.
73
): Raffaele Ferrari, Dena G Hernandez, Michael A Nalls,
Jonathan D Rohrer, Adaikalavan Ramasamy, John BJ Kwok, Carol Dobson-Stone, William S Brooks, Peter R
Schofield, Glenda M Halliday, John R Hodges, Olivier Piguet, Lauren Bartley, Elizabeth Thompson, Eric Haan,
Isabel Hernández, Agustín Ruiz, Mercè Boada, Barbara Borroni, Alessandro Padovani, Carlos Cruchaga,
Nigel J Cairns, Luisa Benussi, Giuliano Binetti, Roberta Ghidoni, Gianluigi Forloni, Diego Albani, Daniela
Galimberti, Chiara Fenoglio, Maria Serpente, Elio Scarpini, Jordi Clarimón, Alberto Lleó, Rafael Blesa, Maria
Landqvist Waldö, Karin Nilsson, Christer Nilsson, Ian RA Mackenzie, Ging-Yuek R Hsiung, David MA Mann,
Jordan Grafman, Christopher M Morris, Johannes Attems, Ian G McKeith, Alan J Thomas, Pietro Pietrini,
Edward D Huey, Eric M Wassermann, Atik Baborie, Evelyn Jaros, Michael C Tierney, Pau Pastor, Cristina
Razquin, Sara Ortega-Cubero, Elena Alonso, Robert Perneczky, Janine Diehl- Schmid, Panagiotis
Alexopoulos, Alexander Kurz, Innocenzo Rainero, Elisa Rubino, Lorenzo Pinessi, Ekaterina Rogaeva, Peter
St George-Hyslop, Giacomina Rossi, Fabrizio Tagliavini, Giorgio Giaccone, James B Rowe, Johannes CM
Schlachetzki, James Uphill, John Collinge, Simon Mead, Adrian Danek, Vivianna M Van Deerlin, Murray
Grossman, John Q Trojanowski, Julie van der Zee, Marc Cruts, Christine Van Broeckhoven, Stefano F Cappa,
Isabelle Leber, Didier Hannequin, Véronique Golfier, Martine Vercelletto, Alexis Brice, Benedetta Nacmias,
Sandro Sorbi, Silvia Bagnoli, Irene Piaceri, Jørgen E Nielsen, Lena E Hjermind, Matthias Riemenschneider,
Manuel Mayhaus, Bernd Ibach, Gilles Gasparoni, Sabrina Pichler, Wei Gu, Martin N Rossor, Nick C Fox, Jason
D Warren, Maria Grazia Spillantini, Huw R Morris, Patrizia Rizzu, Peter Heutink, Julie S Snowden, Sara
Rollinson, Anna Richardson, Alexander Gerhard, Amalia C Bruni, Raffaele Maletta, Francesca Frangipane,
Chiara Cupidi, Livia Bernardi, Maria Anfossi, Maura Gallo, Maria Elena Conidi, Nicoletta Smirne, Rosa
Rademakers, Matt Baker, Dennis W Dickson, Neill R Graff-Radford, Ronald C Petersen, David Knopman,
Keith A Josephs, Bradley F Boeve, Joseph E Parisi, William W Seeley, Bruce L Miller, Anna M Karydas,
Howard Rosen, John C van Swieten, Elise GP Dopper, Harro Seelaar, Yolande AL Pijnenburg, Philip
Scheltens, Giancarlo Logroscino, Rosa Capozzo, Valeria Novelli, Annibale A Puca, Massimo Franceschi,
Alfredo Postiglione, Graziella Milan, Paolo Sorrentino, Mark Kristiansen, Huei-Hsin Chiang, Caroline Graff,
Florence Pasquier, Adeline Rollin, Vincent Deramecourt, Thibaud Lebouvier, Dimitrios Kapogiannis, Luigi
Ferrucci, Stuart Pickering-Brown, Andrew B Singleton, John Hardy, Parastoo Momeni. Acknowledgement:
Intramural funding from the National Institute of Neurological Disorders and Stroke (NINDS) and National
Institute on Aging (NIA), the Wellcome/MRC Centre on Parkinson’s disease, Alzheimer’s Research UK (ARUK,
Grant ARUK-PG2012-18) and by the office of the Dean of the School of Medicine, Department of Internal
Medicine, at Texas Tech University Health Sciences Center. We thank Mike Hubank and Kerra Pearce at the
Genomic core facility at the Institute of Child Health (ICH), University College of London (UCL), for assisting
RF in performing Illumina genotyping experiments (FTD-GWAS genotyping). This study utilized the high-
performance computational capabilities of the Biowulf Linux cluster at the National Institutes of Health,
Bethesda, Md. (http://biowulf.nih.gov). North American Brain Expression Consortium (NABEC) - The work
performed by the North American Brain Expression Consortium (NABEC) was supported in part by the
Intramural Research Program of the National Institute on Aging, National Institutes of Health, part of the US
Department of Health and Human Services; project number ZIA AG000932-04. In addition this work was
supported by a Research Grant from the Department of Defense, W81XWH-09-2-0128. UK Brain Expression
Consortium (UKBEC) - This work performed by the UK Brain Expression Consortium (UKBEC) was supported
by the MRC through the MRC Sudden Death Brain Bank (C.S.), by a Project Grant (G0901254 to J.H. and
M.W.) and by a Fellowship award (G0802462 to M.R.). D.T. was su
pported by the King Faisal Specialist
Ho
spital and Research Centre, Saudi Arabia. Computing facilities used at King's College London were
supported by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's
and St Thomas' NHS Foundation Trust and King's College London. We would like to thank AROS Applied
Biotechnology AS company laboratories and Affymetrix for their valuable input. RF’s work is supported by
Alzheimer’s Society (grant number 284), UK; JBJK was supported by the National Health and Medical Resarch
Council (NHMRC) Australia, Project Grants 510217 and 1005769; CDS was supported by NHMRC Project
Grants 630428 and 1005769; PRS was supported by NHMRC Project Grants 510217 and 1005769 and
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
40
acknowledges that DNA samples were prepared by Genetic Repositories Australia, supported by NHMRC
Enabling Grant 401184; GMH was supported by NHMRC Research Fellowship 630434, Project Grant
1029538, Program Grant 1037746; JRH was supported by the Australian Research Council Federation
Fellowship, NHMRC Project Grant 1029538, NHMRC Program Grant 1037746; OP was supported by NHMRC
Career Development Fellowship 1022684, Project Grant 1003139. IH, AR and MB acknowledge the patients
and controls who participated in this project and the Trinitat Port-Carbó and her family who are supporting
Fundació ACE research programs. CC was supported by Grant P30- NS069329-01 and acknowledges that
the recruitment and clinical characterization of research participants at Washington University were supported
by NIH P50 AG05681, P01 AG03991, and P01 AG026276. LB and GB were supported by the Ricerca
Corrente, Italian Ministry of Health; RG was supported by Fondazione CARIPLO 2009-2633, Ricerca Corrente,
Italian Ministry of Health; GF was supported by Fondazione CARIPLO 2009-2633. ES was supported by the
Italian Ministry of Health; CF was supported by Fondazione Cariplo; MS was supported from the Italian Ministry
of Health (Ricerca Corrente); MLW was supported by Government funding of clinical research within NHS
Sweden (ALF); KN was supported by Thure Carlsson Foundation; CN was supported by Swedish Alzheimer
Fund. IRAM and GYRH were supported by CIHR (grant 74580) PARF (grant C06-01). JG was supported by
the NINDS intramural research funds for FTD research. CMM was supported by Medical Research Council
UK, Brains for Dementia Research, Alzheimer's Society, Alzheimer's Research UK, National Institutes for
Health Research, Department of Health, Yvonne Mairy Bequest and acknowledges that tissue made available
for this study was provided by the Newcastle Brain Tissue Resource, which was funded in part by grants
G0400074 and G1100540 from the UK MRC, the Alzheimer’s Research Trust and Alzheimer’s Society through
the Brains for Dementia Research Initiative and an NIHR Biomedical Research Centre Grant in Ageing and
Health, and NIHR Biomedical Research Unit in Lewy Body Disorders. CMM was supported by the UK
Department of Health and Medical Research Council and the Research was supported by the National Institute
for Health Research Newcastle Biomedical Research Centre based at Newcastle Hospitals Foundation Trust
and Newcastle University and acknowledges that the views expressed are those of the authors and not
necessarily those of the NHS, the NIHR or the Department of Health; JA was supported by MRC, Dunhill
Medical Trust, Alzheimer's Research UK; TDG was supported by Wellcome Trust Senior Clinical Fellow; IGM
was supported by NIHR Biomedical Research Centre and Unit on Ageing Grants and acknowledges the
National Institute for Health Research Newcastle Biomedical Research Centre based at Newcastle Hospitals
Foundation Trust and Newcastle University. The views expressed are those of the author(s) and not
necessarily those of the NHS, the NIHR or the Department of Health; AJT was supported by Medical Research
Council, Alzheimer's Society, Alzheimer's Research UK, National Institutes for Health Research. EJ was
supported by NIHR, Newcastle Biomedical Research Centre. PP, CR, SOC and EA were supported partially
by FIMA (Foundation for Applied Medical Research); PP acknowledges Manuel Seijo-Martínez (Department
of Neurology, Hospital do Salnés, Pontevedra, Spain), Ramon Rene, Jordi Gascon and Jaume Campdelacreu
(Department of Neurology, Hospital de Bellvitge, Barcelona, Spain) for providing FTD DNA samples. RP, JDS,
PA and AK were supported by German Federal Ministry of Education and Research (BMBF; grant number
FKZ 01GI1007A – German FTLD consortium). IR was supported by Ministero dell'Istruzione, dell'Università e
della Ricerca (MIUR) of Italy. PStGH was supported by the Canadian Institutes of Health Research, Wellcome
Trust, Ontario Research Fund. FT was supported by the Italian Ministry of Health (ricerca corrente) and MIUR
grant RBAP11FRE9; GR and GG were supported by the Italian Ministry of Health (ricerca corrente). JBR was
supported by Camrbidge NIHR Biomedical Research Centre and Wellcome Trust (088324). JU, JC, SM were
supported by the MRC Prion Unit core funding and acknowledge MRC UK, UCLH Biomedical Research
Centre, Queen Square Dementia BRU; SM acknowledges the work of John Beck, Tracy Campbell, Gary
Adamson, Ron Druyeh, Jessica Lowe, Mark Poulter. AD acknowledge
s the work of Benedikt Bader and of
Manuel
a Neumann, Sigrun Roeber, Thomas Arzberger and Hans Kretzschmar†; VMVD and JQT were
supported by Grants AG032953, AG017586 and AG010124; MG was supported by Grants AG032953,
AG017586, AG010124 and NS044266; VMVD acknowledges EunRan Suh, PhD for assistance with sample
handling and Elisabeth McCarty-Wood for help in selection of cases; JQT acknowledges Terry Schuck, John
Robinson and Kevin Raible for assistance with neuropathological evaluation of cases. CVB and the Antwerp
site were in part funded by the MetLife Foundation for Medical Research Award (to CVB), the Belgian Science
Policy Office (BELSPO) Interuniversity Attraction Poles program; the Alzheimer Research Foundation (SAO-
FRA); the Medical Foundation Queen Elisabeth (GSKE); the Flemish Government initiated Methusalem
Excellence Program (to CVB); the Research Foundation Flanders (FWO) and the University of Antwerp
Research Fund.. CVB, MC and JvdZ acknowledge the neurologists S Engelborghs, PP De Deyn, A Sieben, R
Vandenberghe and the neuropathologist JJ Martin for the clinical and pathological diagnoses. CVB, MC and
JvdZ further thank the personnel of the Genetic Service Facility of the VIB Department of Molecular Genetics
(http://www.vibgeneticservicefacility.be) and the Antwerp Biobank of the Institute Born-Bunge for their expert
support. IL and AB were supported by the program “Investissements d’avenir” ANR-10-IAIHU-06 and
acknowledges the contribution of The French research network on FTLD/FTLD-ALS for the contribution in
samples collection. BN is founded by Fondazione Cassa di Risparmio di Pistoia e Pescia (grant 2014.0365),
SS is founded by the Cassa di Risparmio di Firenze (grant 2014.0310) and a grant from Ministry of Health n°
RF-2010-2319722. JEN was supported by the Novo Nordisk Foundation, Denmark. MR was supported by the
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
41
German National Genome Network (NGFN); German Ministry for Education and Research Grant Number
01GS0465. JDR, MNR, NCF and JDW were supported by an MRC programme grant and the Dementia
Platform UK, the NIHR Queen Square Dementia Biomedical Research Unit (BRU) and the Leonard Wolfson
Experimental Neurology Centre. MGS was supported by MRC grant n G0301152, Cambridge Biomedical
Research Centre and acknowledges Mrs K Westmore for extracting DNA. HM was supported by the Motor
Neuron Disease Association (Grant 6057). RR was supported by P50 AG016574, R01 NS080882, R01
NS065782, P50 NS72187 and the Consortium for Frontotemporal Dementia; DWD was supported by
P50NS072187, P50AG016574, State of Florida Alzheimer Disease Initiative, & CurePSP, Inc.; NRGR, JEP,
RCP, DK, BFB were supported by P50 AG016574; KAJ was supported by R01 AG037491; WWS was
supported by NIH AG023501, AG019724, Consortium for Frontotemporal Dementia Research; BLM was
supported by P50AG023501, P01AG019724, Consortium for FTD Research; HR was supported by
AG032306. JCvS was supported by Stichting Dioraphte Foundation (11 02 03 00), Nuts Ohra Foundation
(0801-69), Hersenstichting Nederland (BG 2010-02) and Alzheimer Nederland. CG and HHC acknowledge
families, patients, clinicians including Dr Inger Nennesmo and Dr Vesna Jelic, Professor Laura Fratiglioni for
control samples and Jenny Björkström, Håkan Thonberg, Charlotte Forsell, Anna-Karin Lindström and Lena
Lilius for sample handling. CG was supported by Swedish Brain Power (SBP), the Strategic Research
Programme in Neuroscience at Karolinska Institutet (StratNeuro), the regional agreement on medical training
and clinical research (ALF) between Stockholm County Council and Karolinska Institutet, Swedish Alzheimer
Foundation, Swedish Research Council, Karolinska Institutet PhD-student funding, King Gustaf V and Queen
Victoria’s Free Mason Foundation. FP, AR, VD and FL acknowledge Labex DISTALZ. RF acknowledges the
help and support of Mrs. June Howard at the Texas Tech University Health Sciences Center Office of
Sponsored Programs for tremendous help in managing Material Transfer Agreement at TTUHSC.
GOBS: The GOBS study (PI DG and JB) was supported by the National Institute of Mental Health Grants
MH0708143 (Principal Investigator [PI]: DCG), MH078111 (PI: JB), and MH083824 (PI: DCG & JB).
GSP: Brain Genomics Superstruct Project (GSP): Data were provided [in part] by the Brain GSP of Harvard
University and the Massachusetts General Hospital, with support from the Center for BrainScience
Neuroinformatics Research Group, the Athinoula A. Martinos Center for Biomedical Imaging and the Center
for Human Genetic Research. Twenty individual investigators at Harvard and Massachusetts General Hospital
generously contributed data to GSP. This work was made possible by the resources provided through Shared
Instrumentation Grants 1S10RR023043 and 1S10RR023401 and was supported by funding from the Simons
Foundation (RLB), the Howard Hughes Medical Institute (RLB), NIMH grants R01-MH079799 (JWS),
K24MH094614 (JWS), K01MH099232 (AJH), and the Massachusetts General Hospital-University of Southern
California Human Connectome Project (U54MH091665).
HUBIN: This work was supported by the Swedish Research Council (2006-2992, 2006-986, K2007-62X-
15077-04-1, K2008-62P-20597-01-3, 2008-2167, 2008-7573, K2010-62X-15078-07-2, K2012-61X-15078-09-
3, 14266-01A,02-03, 2017-949), the regional agreement on medical training and clinical research between
Stockholm County Council and the Karolinska Institutet, the Knut and Alice Wallenberg Foundation, and the
HUBIN project.
HUNT: The HUNT Study is a collaboration between HUNT Research Centre (Faculty of Medicine and
Movement Sciences, NTNU – Norwegian University of Science and Technology), Nord-Trøndelag County
Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. HUNT-MRI was funded
by the Liaison Committee between the Central Norway Regional He
alth Authority and the Norwegian University
of Science an
d Technology, and the Norwegian National Advisory Unit for functional MRI.
IMAGEN: This work received support from the following sources: the European Union-funded FP6 Integrated
Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT-
2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification
of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural,
Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain
Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), the FP7 projects
IMAGEMEND (602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative
Medicine Initiative Project EU-AIMS (115300-2), the Medical Research Foundation and Medical Research
Council grant MR/R00465X/1; the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to
Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the
Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Centre at
South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür
Bildung und Forschung (BMBF grants 01GS08152; 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz
AERIAL), the Deutsche Forschungsgemeinschaft (DFG grants SM 80/7-1, SM 80/7-2, SFB 940/1). Further
support was provided by grants from: ANR (project AF12-NEUR0008-01 – WM2NA, and ANR-12-SAMA-
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
42
0004), the Fondation de France, the Fondation pour la Recherche Médicale, the Mission Interministérielle de
Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Fondation pour la Recherche Médicale
(DPA20140629802), the Fondation de l’Avenir, Paris Sud University IDEX 2012; the National Institutes of
Health, Science Foundation Ireland (16/ERCD/3797), U.S.A. (Axon, Testosterone and Mental Health during
Adolescence; RO1 MH085772-01A1), and by NIH Consortium grant U54 EB020403, supported by a cross-
NIH alliance that funds Big Data to Knowledge Centres of Excellence.
IMH: We thank the participants, their carers and research support staff of this study. This work was supported
by research grants from the National Healthcare Group, Singapore (SIG/05004; SIG/05028), and the
Singapore Bioimaging Consortium (RP C-009/2006) research grants awarded to KS.
IMpACT: The International Multi-centre persistent ADHD CollaboraTion (IMpACT), is a consortium of clinical
and basic researchers from several European countries (The Netherlands, Germany, Spain, Norway, The
United Kingdom, Sweden), from the United States of America, and from Brazil. In the current study, the
samples from the Netherlands node of IMpACT were used. This work was carried out on the Dutch national e-
infrastructure with the support of SURF Cooperative. This works was supported by the Netherlands
Organization for Scientific Research (Nederlandse Organisatie voor Wetenschappelijk Onderzoek, NWO), i.e.,
the NWO Brain & Cognition Excellence Program (grant 433-09-229) and the Vici Innovation Program (grant
016-130-669 to BF). Additional support was received from the European Community’s Seventh Framework
Programme (FP7/2007–2013) under grant agreements n◦ 602805 (Aggressotype), n◦ 602450 (IMAGEMEND),
and n◦ 278948 (TACTICS) as well as from the European Community’s Horizon 2020 Programme
(H2020/2014–2020) under grant agreements n◦ 643051 (MiND), n◦ 667302 (CoCA), and n◦ 728018
(Eat2beNICE). The work was also supported by grants for the ENIGMA Consortium (Foundation for the
National Institutes of Health (NIH); grant number U54 EB020403) from the BD2K Initiative of a cross-NIH
partnership.
LBC1936: We thank the participants and research support staff of this study. The work was undertaken as
part of the Cross Council and University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology
(CCACE; http://www.ccace.ed.ac.uk). This work was supported by a Research into Ageing programme grant
(to I.J.D.) and the Age UK-funded Disconnected Mind project (http://www.disconnectedmind.ed.ac.uk; to I.J.D.
and J.M.W.), with additional funding from the UK Medical Research Council (MRC Mr/M01311/1,
G1001245/96077, G0701120/79365 to I.J.D., J.M.W. and M.E.B.). The whole genome association part of this
study was funded by the Biotechnology and Biological Sciences Research Council (BBSRC; Ref.
BB/F019394/1). J.M.W. is supported by the Scottish Funding Council through the SINAPSE Collaboration
(http://www.sinapse.ac.uk). CCACE (MRC MR/K026992/1) is funded by the BBSRC and MRC. The image
acquisition and analysis was performed at the Brain Research Imaging Centre, University of Edinburgh
(http://www.bric.ed.ac.uk). MVH is supported by the Row Fogo Charitable Trust.
LIBD: This work was supported by direct funding from the NIMH intramural research program of the NIH to
the Weinberger Lab and by support from the LIeber Institute for Brain Development and the Maltz Research
Laboratories.
MCIC: The authors wish to thank our many colleagues who served as mentors, advisors and supporters during
the inception and conduct of the study including Donald Goff, Gina Kuperberg, Jill Goldstein, Martha Shenton,
Robert McCarley, Stephan Heckers, Cynthia Wible, Raquelle Mesho
lam-Gately, and Mark Vangel. We thank
the
study staff and clinicians at each site that were responsible for the data acquisition. These include: Stuart
Wallace, Ann Cousins, Raquelle Mesholam-Gately, Steven Stufflebeam, Oliver Freudenreich, Daphne Holt,
Laura Kunkel, Frank Fleming, George He, Hans Johnson, Ron Pierson, Arvind Caprihan, Phyllis Somers,
Christine Portal, Kaila Norman, Diana South, Michael Doty and Haley Milner. We would also like to
acknowledge the expert guidance on image and other types of data acquisition we obtained from Lee
Friedman, Stephan Posse, Jorge Jovicich, and Tom Wassink. We would also like to acknowledge the many
research assistants, students and colleagues who assisted in data curation over the years since data
acquisition was completed. These include: Stuart Wallace, Carolyn Zyloney, Komal Sawlani, Jill Fries, Adam
Scott, Dylan Wood, Runtang Wang, William Courtney, Angie Guimaraes, Lisa Shenkman, Mustafa Kendi,
Aysa Tuba Karagulle Kendi, Ryan Muetzel, Tara Biehl, and Marcus Schmidt. This work was supported
primarily by the Department of Energy DE-FG02-99ER62764 through its support of the Mind Research
Network (MRN, formerly known as the MIND Institute) and the consortium as well as by the National
Association for Research in Schizophrenia and Affective Disorders (NARSAD) Young Investigator Award (to
SE) as well as through the Blowitz-Ridgeway and Essel Foundations and a ZonMw TOP 91211021 (to TW),
the DFG research fellowship (to SE), the Mind Research Network, National Institutes of Health through NCRR
5MO1-RR001066 (MGH General Clinical Research Center), NIMH K08 MH068540, the Biomedical Informatics
Research Network with NCRR Supplements to P41 RR14075 (MGH), M01 RR 01066 (MGH), NIBIB
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
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43
R01EB006841 (MRN), R01EB005846 (MRN), 2R01 EB000840 (MRN), 1RC1MH089257 (MRN), as well as
grant U24 RR021992.
Meth-CT: This study was supported by the Medical Research Council, South Africa.
MIRECC: We thank the US military veterans who participated in this research. The Study was supported by
National Institute of Mental Health (1R01MH111671) and the US Department of Veterans Affairs (VISN6
MIRECC).
MooDS: This work was supported by the German Ministry for Education and Research (BMBF) grants (BMBF
National Genome Research Network: NGFN-Plus MooDS “Systematic Investigation of the Molecular Causes
of Major Mood Disorders and Schizophrenia”; see under http://www.ngfn.de/en/schizophrenie.html; e:Med
Programme: Integrated Network IntegraMent, Integrated Understanding of Causes and Mechanisms in Mental
Disorders, grant 01ZX1314A to MMN, grant 01ZX1614A to FD and MMN), and supported by grants from the
German Research Foundation the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG,
FOR 1617 as well as Excellence Cluster Exc 257).
MPIP: The MPIP Munich Morphometry Sample comprises images acquired as part of the Munich
Antidepressant Response Signature (MARS) Study and the Recurrent Unipolar Depression (RUD) Case-
Control study performed at the MPIP, and control subjects acquired at the Ludwig-Maximilians-University,
Munich, Department of Psychiatry. We would like to acknowledge all patients and control subjects who have
participated in these studies. We are grateful to Rosa Schirmer, Elke Schreiter, Reinhold Borschke, Ines Eidner
and Anna Olynyik for supporting MR acquisition and data management. We thank the staff of the Center of
Applied Genotyping for generating the genotypes of the MARS cohort. We further thank Dorothee P. Auer for
initiating the RUD-MR substudy and Elisabeth Binder for supporting participation in ENIGMA. We are grateful
to GlaxoSmithKline for providing the genotypes of the Recurrent Unipolar Depression Case-Control Sample.
The study was supported by a grant of the Exzellenz-Stiftung of the Max Planck Society. This work has also
been funded by the Federal Ministry of Education and Research (BMBF) in the framework of the National
Genome Research Network (NGFN), FKZ 01GS0481.
MPRC: Support was received from NIH grants U01MH108148, 2R01EB015611, R01DA027680,
R01MH085646, P50MH103222, U54 EB020403, and T32MH067533, NSF grants IIS-1302755 and MRI-
1531491, a State of Maryland contract (M00B6400091), and a Pfizer research grant.
MÜNSTER: This work was funded by the German Research Foundation (SFB-TRR58, Projects C09 and Z02
to UD) and the Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster (grant
Dan3/012/17 to UD).
NCNG: The study was supported by the Bergen Research Foundation (BFS), the University of Bergen, the
Research Council of Norway (RCN) (including FUGE grant nos. 151904 and 183327, Psykisk Helse grant no.
175345, RCN grants 154313/V50 to I.R. and 177458/V50 to T.E.), Helse Sørøst RHF to T.E. (grant 2012086),
and Dr Einar Martens Fund.
NESDA: The infrastructure for the NESDA study (www.nesda.nl) is funded through the Geestkracht program
of the Netherlands Organisation for Health Research and Development (Zon-Mw, grant number 10-000-1002)
and is supported by participating universities (VU University Medical Center, GGZ inGeest, Arkin, Leiden
University Medical Center, GGZ Rivierduinen, University Medical Center Groningen) and mental health care
organizations.Funding was obtained from the Netherlands Organization for Scientific Research (Geestkracht
program grant 10-000-1002); the Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking
and Biomolecular Resources Research Infrastructure (BBMRI-NL), VU University’s Institutes for Health and
Care Research (EMGO+) and Neuroscience Campus Amsterdam, University Medical Center Groningen,
Leiden University Medical Center, National Institutes of Health (NIH, R01D0042157-01A, MH081802, Grand
Opportunity grants 1RC2 MH089951 and 1RC2 MH089995). Part of the genotyping and analyses were funded
by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of
Health.Computing was supported by BiG Grid, the Dutch e-Science Grid, which is financially supported by
NWO.
NeuroIMAGE: The NeuroIMAGE study was supported by NIH Grant R01MH62873(to Stephen V. Faraone),
NWO Large Investment Grant 1750102007010(to Jan Buitelaar), ZonMW grant 60-60600-97-193, NWO
grants 056-13-015 and 433-09-242, and matching grants from Radboud University Nijmegen Medical Center,
University Medical Center Groningen and Accare, and Vrije Universiteit Amsterdam. Further support was
received from the European Union FP7 programmes TACTICS (grant agreement 278948), IMAGEMEND
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
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44
(grant agreement 602450), Aggressotype (grant agreement 602805), CoCA (grant agreement 667302), and
Eat2beNICE (grant agreement 728018).
NTR: Netherlands Twin Register: Funding was obtained from the Netherlands Organization for Scientific
Research (NWO) and The Netherlands Organisation for Health Research and Development (ZonMW) grants
904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717,
Addiction-31160008, 016-115-035, 481-08-011, 056-32-010, Middelgroot-911-09-032, OCW_NWO Gravity
program –024.001.003, NWO-Groot 480-15-001/674, Center for Medical Systems Biology (CSMB, NWO
Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure
(BBMRI –NL, 184.021.007 an 184.033.111); Spinozapremie (NWO- 56-464-14192), KNAW Academy
Professor Award (PAH/6635) and University Research Fellow grant (URF) to DIB; Amsterdam Public Health
research institute (former EMGO+) , Neuroscience Amsterdam research institute (former NCA) ; the European
Science Foundation (ESF, EU/QLRT-2001-01254), the European Community's Seventh Framework Program
(FP7- HEALTH-F4-2007-2013, grant 01413: ENGAGE and grant 602768: ACTION); the European Research
Council (ERC Advanced, 230374, ERC Starting grant 284167), Rutgers University Cell and DNA Repository
(NIMH U24 MH068457-06), the National Institutes of Health (NIH, R01D0042157-01A1, R01MH58799-03,
MH081802, DA018673, R01 DK092127-04, Grand Opportunity grants 1RC2 MH089951, and 1RC2
MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping
and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the
National Institutes of Health. Computing was supported by NWO through grant 2018/EW/00408559, BiG Grid,
the Dutch e-Science Grid and SURFSARA.
OATS: We would like to thank the participants and their supporters for their time and generosity. We
acknowledge and thank the contributions of the OATS research team. OATS is supported by the Australian
National Health and Medical Research Council (NHMRC)/Australian Research Council Strategic Award (Grant
401162) and the NHMRC Project grant 1405325. This study was facilitated through Twins Research Australia,
a national resource in part supported by a Centre for Research Excellence from the NHMRC. DNA was
extracted by Genetic Repositories Australia (NHMRC Grant 401184). Genome-wide genotyping was
performed by the Diamantina Institute, University of Queensland. A CSIRO Flagship Collaboration Fund Grant
partly funded the genotyping.
OSAKA: This research was supported by AMED under Grant Number JP18dm0307002, JP18dm0207006
(Brain/MINDS) and JSPS KAKENHI Grant Number J16H05375.
PAFIP: The authors wish to thank all PAFIP research team and all patients and family members who
participated in the study. We wish to acknowledge IDIVAL Neuroimaging Unit for imaging acquirement and
analysis. We thank Valdecilla Biobank for its help in the technical execution of this work. This work was
supported by the Instituto de Salud Carlos III (PI14/00639 and PI14/00918), MINECO (SAF2010-20840-C02-
02 and SAF2013-46292-R) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and
NCT02534363). No pharmaceutical company has financially supported the study.
PDNZ: We thank the patients who participated in this study, staff at the New Zealand Brain Research Institute
and Pacific Radiology Christchurch for study co-ordination and image acquisition, and Ms Allison Miller for
DNA preparation and banking. Neurological Foundation of New Zealand; Canterbury Medical Research
Foundation; University of Otago Research Grant; Jim and Mary Carney Charitable Trust (Whangarei, New
Zealand).
PING: PING was supported by the National Institute on Drug Abuse (RC2DA029475) and the National Institute
of Child Health and Human Development (R01HD061414) in the U.S.
PPMI: Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers
Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-
info.org. Parkinson's Progression Markers Initiative, a public-private partnership, is funded by the Michael J.
Fox Foundation for Parkinson’s Research and funding partners, including AbbVie, Allegran, Avid
Radiopharmaceuticals, Biogen Idec, BioLegend, Bristol-Meyers Squibb, Denali Therapeutics, GE Healthcare,
Genentech, GSK-GlaxoSmithKline, Eli Lilly & Co., F. Hoffman-La Roche Ltd., Lundbeck Pharmaceuticals,
Merck and Company, MSD-Meso Scale Discovery, Pfizer, Piramal, Sanofi Genzyme, Servier, Takeda
Pharmaceutical Company, TEVA Pharmaceutical Industries, UCB Pharma SA, and Golub Capital
(http://www.ppmi-info.org/about-ppmi/who-we-are/study-sponsors/). Support for the image and genetic data
analysis of PPMI was provided in part by MJFF grant number 14848 (to N.J.)
QTIM: We thank the twins and singleton siblings who gave generously of their time to participate in the QTIM
study. We also thank the many research assistants, radiographers, and IT support staff for data acquisition
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
45
and DNA sample preparation. National Institute of Child Health & Human Development (R01 HD050735);
National Institute of Biomedical Imaging and Bioengineering (Award 1U54EB020403-01, Subaward
56929223); National Health and Medical Research Council (Project Grants 496682, 1009064 and Medical
Bioinformatics Genomics Proteomics Program 389891).
SHIP and SHIP/TREND: SHIP is part of the Community Medicine Research net of the University of Greifswald,
Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603,
01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State
of Mecklenburg-West Pomerania, and the network ‘Greifswald Approach to Individualized Medicine
(GANI_MED)’ funded by the Federal Ministry of Education and Research (grant 03IS2061A). Whole-body MR
imaging was supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State
of Mecklenburg West Pomerania. Genome-wide data have been supported by the Federal Ministry of
Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen,
Germany and the Federal State of Mecklenburg- West Pomerania. The University of Greifswald is a member
of the Caché Campus program of the InterSystems GmbH. The SHIP authors are grateful to Mario Stanke for
the opportunity to use his Server Cluster for the SNP imputation as well as to Holger Prokisch and Thomas
Meitinger (Helmholtz Zentrum München) for the genotyping of the SHIP-Trend cohort.
Sydney MAS: We would like to thank and acknowledge the generosity of our participants and their supporters
in contributing to this study. We would also like to thank the Sydney MAS Research Team. Sydney MAS is
supported by the National Health and Medical Research Council (NHMRC)/Australian Research Council
Strategic Award (Grant 401162) and NHMRC Program Grants (350833, 568969). DNA was extracted by
Genetic Repositories Australia (NHMRC Grant 401184). Genome-wide genotyping was performed by the
Ramaciotti Centre, University of New South Wales.
SYS: The SYS has been funded by the Canadian Institutes of Health Research and the Heart and Stroke
Foundation of Canada. Computations were performed on the GPC supercomputer at the SciNet HPC
Consortium. SciNet is funded by: the Canada Foundation for Innovation under the auspices of Compute
Canada; the Government of Ontario; Ontario Research Fund - Research Excellence; and the University of
Toronto.
TCD-NUIG: Included data from two sites. NUI Galway: data collection was supported by the Health Research
Board (HRA_POR/2011/100). Trinity College Dublin: this research was supported by The Science Foundation
Ireland Research Investigator project, awarded to Gary Donohoe (SFI: 12.IP.1359).
TOP and TOP3T: are part of TOP, which is supported by the Research Council of Norway (223273, 213837,
249711, 226971, 262656), the South East Norway Health Authority (2017-112), the Kristian Gerhard Jebsen
Stiftelsen (SKGJ-MED-008) and the European Community's Seventh Framework Programme (FP7/2007–
2013), grant agreement no. 602450 (IMAGEMEND).
UiO2016 and UiO2017: are part of TOP and STROKEMRI, which is supported by the Norwegian
ExtraFoundation for Health and Rehabilitation (2015/FO5146), the Research Council of Norway (249795,
248238), and the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073).
UK Biobank: This research has been conducted using the UK Biobank Resource under Application Number
‘11559’.
UMCU: The UMCU cohort consists of several independent studies, whic
h were supported by The Netherlands
Orga
nisation for Health Research and Development (ZonMw) TOP 40-008-12-98-13009, Geestkracht
programme of the Netherlands Organisation for Health Research and Development (ZonMw, grant number
10-000-1001), the Stanley Medical Research Institute (Dr. Nolen), the Brain and Behavior Research
Foundation (2013-2015 NARSAD Independent Investigator grant number 20244 to M.H.J.H.), The
Netherlands Organisation for Scientific Research (2012-2017 VIDI grant number 452-11-014 to N.E.M.H.),
The Netherlands Organisation for Health Research and Development (ZonMw grant number 908-02-123 to
H.E.H.), The Netherlands Organisation for Scientific Research (VIDI grant number 917-46-370 to H.E.H.).
UNICAMP: Supported by FAPESP (São Paulo Research Foundation) grant #2013/07559-3: The Brazilian
Institute of Neuroscience and Neurotechnology (BRAINN).
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
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46
Author Contributions (in alphabetical order)
K.L.G. and N.J. contributed to this work as co-first authors (*). J.N.P., L.C.-C., J.B., D.P.H., P.A.L., F.P.
contributed to this work as co-second authors. J.L.S., P.M.T., S.E.M. contributed to this work as co-last authors
(**).
Central Analysis and Coordination Group
C.R.K.C., D.P.H., F.P., J.Br., J.L.S., J.N.P., K.L.G., L.C.-C., L.Z., M.A.B.M., N.J., N.S., P.A.L., P.M.T., S.E.M.
Manuscript Writing and Preparation
D.P.H., J.Br., J.L.S., J.N.P., K.L.G., L.C.-C., N.J., P.A.L., P.M.T., S.E.M.
Project Support
D.Ga., M.A.B.M., M.J., N.S., R.E., V.R., Y.G.
Cohort Principal Investigator
A.A.V., A.C., A.H., A.J.F., A.J.H., A.K.H., A.M.D., A.M.-L., A.R.H., A.W.T., B.C.-F., B.S.P., B.T.B., B.W.,
B.W.J.H.P., C.A.H., C.Dep., C.F., C.M., D.Am., D.C.G., D.I.B., D.J.S., D.P., D.R.W., D.v.E., E.G.J., E.J.C.d.G.,
E.L.H., F.C., G.D., G.F., G.G.B., G.L.C., G.S., H.B., H.E.H.P., H.F., H.G.B., H.J.G., H.V., H.W., I.A., I.E.Som.,
I.J.D., I.M., J.B.J.K., J.Bl., J.C.D.-A., J.K.B., J.-L.M., J.L.R., J.N.T., J.O., J.R.B., J.W.S., J.Z., K.L.M., K.S.,
L.M.R., L.N., L.R., L.T.W., M.E.B., M.H.J.H., M.J.W., M.K.M.A., M.R., N.D., N.J., N.J.A.v.d.W., O.A.A., O.G.,
P.G.S., P.J.H., P.K., P.M.T., P.S.S., R.A.M., R.A.O., R.H., R.L.B., R.L.G., R.S.K., S.Ca., S.Des., S.E.F., S.L.H.,
S.M.S., S.R., T.E., T.J.A., T.J.C.P., T.L.J., T.P., T.T.J.K., U.D., V.C., W.C., W.U.H., X.C., Z.P.
Imaging Data Collection
A.B., A.d.B., A.F.M., A.J., A.J.H., A.K., A.K.H., A.L.G., A.M.D., A.N.H., A.P., A.R.H., A.R.K., A.U., B.A.M., B.-
C.H., B.D., B.Pi., B.W., B.W.J.H.P., C.B., C.D.W., C.J., C.L.B., C.L.Y., C.M., C.R.J., C.S.Re., D.Am., D.C.G.,
D.Gr., D.H.M., D.J., D.J.H., D.J.V., D.M.C., D.P.O., D.R.W., D.S.O., D.T.-G., D.v.E., D.v.R., D.Z., E.A., E.B.Q.,
E.J.C.d.G., E.L.H., E.Sh., G.B.P., G.D., G.F., G.I.d.Z., G.L.C., G.R., G.S., H.V., H.Y., I.A., I.E.Som., J.A.T.,
J.E.C., J.E.N., J.K.B., J.-L.M., J.-L.M., J.L.R., J.M.F., J.M.W., J.N.T., J.R., J.T.V., K.D., K.K., K.L.M., K.O.L.,
K.S., L.M.R., L.R., L.T.W., M.B.H., M.E.B., M.Fu., M.H.J.H., M.Ho., M.-J.v.T., M.J.W., M.-L.P.M., N.E.M.v.H.,
N.H., N.J.A.v.d.W., N.K.H., N.O., O.G., P.A.G., P.G.S., P.K., P.N., P.S.S., R.A.O., R.B., R.H., R.L.B., R.L.G.,
R.R., R.S.K., R.W., S.A., S.C.M., S.Ca., S.Er., S.Ko., S.M., S.M.S., T.G.M.v.E., T.R.M., T.Wh., T.W.M., U.D.,
V.C., V.S.M., W.D.H., W.H., W.W., X.C.
Imaging Data Analysis
A.F.M., A.H.Z., A.J.H., A.J.S., A.L.G., A.M.D., A.R., A.R.K., A.S., A.Th., A.U., B.A.G., B.C.R., B.K., B.S.P.,
C.B., C.C.F., C.C.H., C.D.W., C.J., C.L.Y., C.R.K.C., C.S.Ro., D.Al., D.C.G., D.Gr., D.H., D.J., D.J.H., D.M.C.,
D.P.H., D.P.O., D.T.-G., D.v.d.M., D.v.E., D.v.R., D.Z., E.E.L.B., E.Sh., E.Sp., E.W., F.M.R., F.P., F.S., G.I.d.Z.,
G.R., H.J.G., I.A., I.E.Som., I.K.A., J.A.T., J.B.J.K., J.C.V.M., J.-L.M., J.L.R., J.L.S., J.M.W., J.R., J.Z., K.D.,
K.L.M., K.N., K.S., K.W., L.B.L., L.H., L.Sa., L.Sc., L.Sh., L.T.S., L.T.W., L.v.E., L.Z., M.A., M.A.H., M.B.H.,
M.C., M.E.B., M.Fu., M.Ho., M.-J.v.T., M.J.W., M.Ki., M.P.Z., N.E.M.v.H., N.J., N.O., N.T.D., O.G., P.G.S.,
P.K., P.M.T., P.N., R.B., R.K., R.L.G., R.M.B., R.R., R.R.-S., S.A., S.Ca., S.Des., S.Eh., S.Er., S.F.F., S.I.T.,
S.Ka., S.Ke., S.L.R., S.M.C.d.Z., S.R.M., T.A., T.A.L., T.G., T.G.M.v.E., T.J., T.K., T.L.P., T.P.G., T.R.M.,
T.Wh., T.Wo., T.W.M., U.D., W.W., X.C., Z.Z.
Genetic Data collection
A.A.A., A.A.-K., A.d.B., A.J.F., A.J.H., A.J.S., A.K.H., A.M.D., A.P., A.R.H., A.R.K., B.-C.H., B.T.B., B.W.,
B.W.J.H.P., C.B., C.D.W., C.F., C.M., C.P.D., C.S.Re., D.C.G., D.H.M., D.R.W., D.W.M., D.Z., E.A., E.B.Q.,
E.G.J., E.J.C.d.G., E.L.H., F.D., F.M., F.R.T., G.D., G.E.D., G.F., G.H., G.L.C., G.S., H.V., H.Y., I.E.Som., I.L.-
C., J.A.T., J.B.J.K., J.Bl., J.E.C., J.E.N., J.-J.H., J.J.L., J.K.B., J.-L.M., J.-L.M., J.L.R., J.M.F., J.R., J.W.S.,
K.A.M., K.D., K.O.L., K.S., L.M.R., L.R., L.Sh., M.A.K., M.H.J.H., M.Ha., M.Ho., M.J.W., M.-L.P.M., M.M.N.,
M.N., N.E.M.v.H., N.G.M., N.J.A.v.d.W., N.K.H., N.O., O.G., P.K., P.R.S., P.S.S., R.A.O., R.C.G., R.H., R.L.B.,
R.R., R.Se., R.S.K., R.W., S.A., S.Ci., S.Dj., S.E.F., S.Eh., S.Er., S.L.H., S.M.S., T.G.M.v.E., T.J.A., T.K.d.A.,
T.L.P., T.W.M., U.D., V.C., V.M.S., X.C.
Genetic Data Analysis
A.A.-K., A.J.F., A.J.H., A.J.S., A.M.D., A.R.K., A.Te., A.Th., B.C.-D., B.K., B.M.-M., B.Pü., B.S.P., B.T.B.,
C.C.F., C.D.W., C.L.V., C.S.Re., C.S.Ro., C.W., C.Y.S., D.C.G., D.K., D.P.H., D.v.d.M., D.v.E., E.G.J., E.L.H.,
E.V., E.W., F.M., H.-R.E., I.E.J., I.E.Som., I.E.Søn., I.L.-C., I.O.F., J.Bl., J.Br., J.F.P., J.H.V., J.-J.H., J.L.R.,
J.L.S., J.N.P., J.S., J.W.C., J.W.S., K.E.T., K.L.G., K.N., L.C.-C., L.M.O.L., L.Sh., L.Z., M.A.A.A., M.B., M.E.G.,
M.Fu., M.Ha., M.I., M.J., M.J.W., M.Ki., M.Kl., M.Kn., M.L., M.M.J.v.D., N.A.G., N.G.M., N.J., N.J.A., N.K.H.,
N.M.-S., N.R.M., O.G., P.A.L., P.G.S., P.H.L., P.K., P.M.T., P.R.S., Q.C., R.A.O., R.M.B., R.R., R.Se., S.Da.,
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
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47
S.Des., S.E.M., S.Eh., S.G., S.H.W., S.L.H., S.M.C.d.Z., S.N., S.R.M., T.A.L., T.G., T.G.M.v.E., T.J., T.K.d.A.,
T.M.L., Y.M., Y.W.
CHARGE Study Design
B.M., C.Dec., C.L.S., E.H., G.V.R., H.H.H.A., H.J.G., J.C.B., L.J.L., M.A.I., M.Fo., O.L.L., Q.Y., R.Sc., S.Deb.,
S.S., T.H.M., V.G., W.T.L.
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
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Surface Area P < 8.3e-10
Thickness P < 8.3e-10
Surface Area P < 5e-8
Thickness P < 5e-8
0 Global (SA:8; TH:1)
1 Frontal Pole (SA:1; TH:0)
2 Medial Orbitofrontal (SA:1; TH:0)
3 Lateral Orbitofrontal (SA:3; TH:0)
4 Rostral Anterior Cingulate (SA:0; TH:0)
5 Caudal Anterior Cingulate (SA:2; TH:0)
6 Superior Frontal (SA:1; TH:0)
7 Rostral Middle Frontal (SA:4; TH:0)
8 Pars Orbitalis (SA:4; TH:0)
9 Pars Triangularis (SA:4; TH:0)
10 Pars Opercularis (SA:2; TH:0)
11 Caudal Middle Frontal (SA:3; TH:0)
12 Paracentral (SA:0; TH:0)
13 Precentral (SA:6; TH:0)
14 Postcentral (SA:6; TH:2)
15 Precuneus (SA:5; TH:0)
16 Superior Parietal (SA:10; TH:1)
17 Supramarginal (SA:7; TH:0)
18 Inferior Parietal (SA:9; TH:0)
19 Posterior Cingulate (SA:4; TH:0)
20 Isthmus Cingulate (SA:2; TH:0)
21 Insula (SA:4; TH:0)
22 Entorhinal (SA:1; TH:0)
23 Parahippocampal (SA:2; TH:1)
24 Fusiform (SA:4; TH:0)
25 Temporal Pole (SA:0; TH:0)
26 Inferior Temporal (SA:0; TH:0)
27 Middle Temporal (SA:3; TH:1)
28 Superior Temporal (SA:3; TH:0)
29 Banks of the Superior Temporal Sulcus
(SA:2; TH:1)
30 Transverse Temporal (SA:3; TH:1)
31 Lingual (SA:12; TH:0)
32 Pericalcarine (SA:14; TH:1)
33 Cuneus (SA:4; TH:1)
34 Lateral Occipital (SA:6; TH:0)
0 6 12 18
Number of independent loci
a
b
c
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
regulation of organelle organization
regulation of translation
chromatin modification
PcG protein complex
methyltransferase complex
histone methyltransferase complex
chromatin binding
histone deacetylase binding
nuclear chromosome part
covalent chromatin modification
protein kinase binding
nuclear chromosome
chromatin remodeling complex
histone modification
kinase binding
Wnt receptor signaling pathway
regulation of anatomical structure morphogenesis
canonical Wnt receptor signaling pathway
negative regulation of Wnt receptor signaling pathway
vasculature development
blood vessel development
regulation of Wnt receptor signaling pathway
pattern specification process
regulation of canonical Wnt receptor signaling pathway
regionalization
blood vessel morphogenesis
negative regulation of canonical Wnt receptor signaling pathway
tube morphogenesis
head development
embryo development ending in birth or egg hatching
a b
c
d
Correlation
Correlation
0 1 2 3 4 5 6
-log10(P-value)
0 1 2 3 4 5 6
-log10(P-value)
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
Parkinson's disease
General cognitive function
Educational attainment
Ano
rexia nervosa
Bipolar disorder
Obsessive compulsive disorder
Autism
Epilepsy
Frontotemporal dementia
Subjective well-being
Irritable bowel disease
Anxiety
Cigarettes per day
Schizophrenia
Aggression
Antisocial behavior
Loneliness
Neuroticism
Major depressive disorder
Depressive symptoms
Alzheimer's disease
ADHD
PTSD
Insomnia
a
b
Parkinson's
Disease
General cognitive
function
Major Depressive
Disorder
ADHD
Insomnia
-.4 -.2 0 .2 .4
-.4 -.2 0 .2 .4
Genetic Correlation (r
G
)
-.3 -.2 -.1 0 .1 .2 .3
Genetic Correlation (r
G
)
.CC-BY-NC-ND 4.0 International licensenot peer-reviewed) is the author/funder. It is made available under a
The copyright holder for this preprint (which was. http://dx.doi.org/10.1101/399402doi: bioRxiv preprint first posted online Sep. 3, 2018;
